[{"data":1,"prerenderedAt":42180},["ShallowReactive",2],{"/podcast/deep-dive-205-ai-evals-mit-martin-seeler":3},{"podcast":4,"pickOfTheDayCount":106,"speakerCount":107,"relatedPodcasts":108,"transcript":109},{"id":5,"published_on":6,"type":7,"number":8,"title":9,"slug":10,"description":11,"transcript":12,"audio_url":13,"apple_url":12,"google_url":12,"spotify_url":12,"cover_image":14,"banner_image":26,"speakers":36,"members":67,"picks_of_the_day":101,"tags":102,"tagsPrepared":103,"speakersPrepared":104},"0d7349d4-10fe-4dc1-999b-50b04a618b65","2026-04-14T04:00:00.000Z","deep_dive","205","AI Evals mit Martin Seeler","deep-dive-205-ai-evals-mit-martin-seeler","\u003Cp>Ein Modell-Update bei OpenAI oder Anthropic ist schnell gemacht. Aber wie stellt ihr sicher, dass eure Anwendung danach noch genau das tut, was sie soll?\u003C/p>\n\u003Cp>In dieser Folge, aufgenommen auf der DecompileD in Dresden, sprechen wir mit Martin Seeler, Senior Staff AI Engineer bei \u003Ca href=\"https://www.blueyonder.com/\">Blue Yonder\u003C/a>, über die Welt der AI Evals und den Unterschied zwischen einem reinen „Vibe-Check“ und belastbarer Teststrategie für GenAI-Produkte.\u003C/p>\n\u003Cp>Im Fokus stehen die drei Säulen der Evaluation: Code-based Evals, LLM-as-a-Judge und Human-in-the-loop. Außerdem geht es um Error Analysis, Failure Modes und wie ihr aus Logs durch Clustering eine eigene Fehler-Taxonomie entwickelt.\u003C/p>\n\u003Cp>Wir besprechen, warum binäre Bewertungen (True/False) oft hilfreicher sind als Scores, wie ihr Kosten, Latenz und Qualität gegeneinander abwägt und wie Tools wie \u003Ca href=\"https://langfuse.com/\">Langfuse\u003C/a>, \u003Ca href=\"https://arize.com/phoenix/\">Phoenix\u003C/a>, \u003Ca href=\"https://www.promptfoo.dev/\">promptfoo\u003C/a> oder \u003Ca href=\"https://www.braintrust.dev/\">Braintrust\u003C/a> euch beim Monitoring und Testing unterstützen.\u003C/p>\n\u003Cp>Wenn ihr wissen möchtet, wie ihr eure KI-Anwendung vom Prototypen in einen stabilen Enterprise-Betrieb überführt und Evals gezielt für Fine-Tuning oder Reinforcement Learning nutzt, ist dieser Deep Dive genau richtig für euch.\u003C/p>\n\u003Cp>Vielen Dank an das Team der DecompileD für die Gastfreundschaft und an Vodafone für die Bereitstellung der Räumlichkeiten mit Blick auf die Trainingsfelder von Dynamo Dresden!\u003C/p>",null,"https://www.buzzsprout.com/176239/episodes/19006232-deep-dive-205-ai-evals-mit-martin-seeler.mp3",{"id":15,"title":16,"description":12,"tags":12,"location":12,"filename_disk":17,"filename_download":18,"metadata":19,"type":20,"filesize":21,"modified_by":12,"modified_on":22,"embed":12,"uploaded_by":23,"uploaded_on":24,"folder":12,"width":25,"height":25,"charset":12,"duration":12},"1eec24e6-f6b8-44fa-a5a6-4e7720c800f9","Deep Dive Cover Workflow","1eec24e6-f6b8-44fa-a5a6-4e7720c800f9.png","Deep Dive Cover Workflow.png",{},"image/png","5531286","2026-04-10T12:04:33.252Z","0bccec21-651a-4e94-a466-2ae142e34e0b","2026-04-10T12:04:33.249Z",2048,{"id":27,"title":28,"description":12,"tags":12,"location":12,"filename_disk":29,"filename_download":30,"metadata":31,"type":20,"filesize":32,"modified_by":12,"modified_on":33,"embed":12,"uploaded_by":23,"uploaded_on":34,"folder":12,"width":35,"height":35,"charset":12,"duration":12},"a029e907-b089-4185-aa91-c904423cc75e","Linked in Profilbild Ohne Hintergrund","a029e907-b089-4185-aa91-c904423cc75e.png","LinkedIn Profilbild ohne Hintergrund.png",{},"115392","2026-04-10T12:10:28.913Z","2026-04-10T12:10:28.909Z",500,[37],{"speaker":38},{"id":39,"slug":40,"academic_title":12,"occupation":41,"first_name":42,"last_name":43,"description":44,"event_image":45,"profile_image":57},"6a2b9872-8c84-49c2-80af-4065821efde3","martin-seeler","Sr Staff AI Engineer bei Blue Yonder","Martin","Seeler","\u003Cp>Martin Seeler verantwortet als Senior Staff AI Engineer bei Blue Yonder GenAI-Strategie und -Systeme für globale Supply Chains. Seine Arbeit beeinflusst, wie einige der größten Unternehmen der Welt ihre Lieferketten steuern. Er hat bereits über 15 Jahre Erfahrung in der Software-Entwicklung für etliche Kund:innen und Branchen, von großen Online-Shops bis zu Enterprise-Plattformen.\u003C/p>\n\u003Cp>Zum Kaffee liest Martin gern Paper und probiert Dinge selbst aus, bevor er sie empfiehlt. Denn am Ende adaptiert er nur das, wovon er überzeugt ist, sodass es wütende Kund:innen, skeptische Executives und Black-Friday-Traffic überlebt.\u003C/p>",{"id":46,"title":47,"description":12,"tags":12,"location":12,"filename_disk":48,"filename_download":49,"metadata":50,"type":51,"filesize":52,"modified_by":12,"modified_on":53,"embed":12,"uploaded_by":54,"uploaded_on":55,"folder":12,"width":56,"height":56,"charset":12,"duration":12},"0b23e683-6ce1-4f9d-8900-ddc44cd8ea00","Signal 2025 04 24 12 07 04 330 15 Edit","0b23e683-6ce1-4f9d-8900-ddc44cd8ea00.jpg","signal-2025-04-24-12-07-04-330-15-EDIT.jpg",{},"image/jpeg","179565","2026-04-04T11:38:16.933Z","ef1f3dcc-dd4b-4911-958e-6e6f729a2f08","2026-04-04T11:38:16.927Z",1094,{"id":58,"title":59,"description":12,"tags":12,"location":12,"filename_disk":60,"filename_download":61,"metadata":62,"type":20,"filesize":63,"modified_by":12,"modified_on":64,"embed":12,"uploaded_by":54,"uploaded_on":65,"folder":12,"width":66,"height":66,"charset":12,"duration":12},"fba725ed-c84a-470e-b05d-50ab5fbb2a35","Linked in Profilbild (2)","fba725ed-c84a-470e-b05d-50ab5fbb2a35.png","LinkedIn Profilbild (2).png",{},"382502","2026-04-04T11:38:16.558Z","2026-04-04T11:38:16.555Z",800,[68,84],{"member":69},{"id":70,"first_name":71,"last_name":72,"occupation":73,"description":74,"normal_image":75},"958f6624-c721-4ff9-8a97-69148f540945","Dennis","Becker","// Podcast-Host","\u003Cp>Dennis ist Head of Development bei Lotum und kümmert sich liebevoll um die Entwickler:innen und deren Weiterentwicklung. \u003Cbr>In den Podcastfolgen übernimmt Dennis gerne die Moderation. Beim täglichen Tischtennis ist er immer am Start und seit wenigen Jahren passionierter Kitesurfer. Mittlerweile ist er auch Vibe Coder mit Leidenschaft und überzeugt, dass seine Kolleg:innen AI unterschätzen. \u003C/p>",{"id":76,"title":77,"description":12,"tags":12,"location":12,"filename_disk":78,"filename_download":79,"metadata":12,"type":51,"filesize":80,"modified_by":12,"modified_on":81,"embed":12,"uploaded_by":12,"uploaded_on":82,"folder":12,"width":83,"height":83,"charset":12,"duration":12},"7a9fd72e-16b1-4283-8c1f-348a3e7986f9","Dennis Becker","7a9fd72e-16b1-4283-8c1f-348a3e7986f9.jpg","dennis_becker_normal_01e352e682.jpg","45729","2022-01-16T14:26:35.196Z","2021-12-30T14:16:28.647Z",1024,{"member":85},{"id":86,"first_name":87,"last_name":88,"occupation":89,"description":90,"normal_image":91},"11d2e9b0-4443-4287-96bc-3930b0ada739","Dave","Koschitzki","// Website // Podcast-Host","\u003Cp>Dave ist seit 2024 Mitglied des programmier.bar-Teams. Neben seiner Arbeit an der Website-Entwicklung bringt er frischen Wind in den Podcast und ist regelmäßig bei Meetups anzutreffen. Seine Leidenschaft für Games, die er sowohl auf der Konsole als auch am PC spielt, ist unübersehbar.\u003Cbr> \u003Cbr>Wenn er nicht gerade in virtuelle Welten eintaucht, trifft man ihn im Fitnessstudio, wo er mit Begeisterung Gewichte stemmt. Dave bereichert das Team nicht nur durch seine technischen Fähigkeiten, sondern auch durch seine vielseitigen Interessen und ansteckende Energie.\u003C/p>",{"id":92,"title":93,"description":12,"tags":12,"location":12,"filename_disk":94,"filename_download":95,"metadata":96,"type":51,"filesize":97,"modified_by":12,"modified_on":98,"embed":12,"uploaded_by":99,"uploaded_on":100,"folder":12,"width":83,"height":83,"charset":12,"duration":12},"578c2a0f-e919-4668-9cb4-f5b97252d57f","Dave CD","578c2a0f-e919-4668-9cb4-f5b97252d57f.jpg","Dave_CD.jpg",{},"675668","2024-07-29T14:46:16.725Z","e97179d2-51b3-4deb-828f-9524091441f5","2024-07-29T14:46:16.613Z",[],[],[],[105],{"first_name":42,"last_name":43,"profile_image":57,"slug":40,"description":44,"event_image":45,"academic_title":12},461,191,[],{"id":110,"service":111,"supported_features":112,"speakers":115,"raw_response":122},"7d42c5ed-4c88-4470-a08c-59f72804e774","deepgram",[113,114],"timestamps","diarization",[116,118,120],{"identifier":117,"name":71},"0",{"identifier":119,"name":87},"1",{"identifier":121,"name":42},"2",{"metadata":123,"results":137},{"transaction_key":124,"request_id":125,"sha256":126,"created":127,"duration":128,"channels":129,"models":130,"model_info":132},"deprecated","019d86b6-7fe0-75a2-8d1e-e8c9458944a3","61b0c4c5e3d9526429691349d589bbb6236c04230994e238fe15afb253847ac0","2026-04-13T12:00:11.740Z",3781.9495,1,[131],"91f8eabe-2126-4ed4-9e56-d480032309b3",{"91f8eabe-2126-4ed4-9e56-d480032309b3":133},{"name":134,"version":135,"arch":136},"2-general-nova","2024-05-09.3071","nova-2",{"channels":138,"utterances":28528},[139],{"alternatives":140},[141],{"transcript":142,"confidence":143,"words":144,"paragraphs":26506},"Hallo und herzlich willkommen zu 1 neuen Programmierbar Folge, einem Deep Dive, wo wir heute über AI Evils sprechen. Das in 'nem besonderen Setting, wir sind nämlich nicht in unserem Studio zu Hause, sondern mit 'ner ganz schönen Aussicht und ich hab gehört auf die Trainingsflächen des, oh, sag schnell, wie der Dresdner Fußballklub heißt. Dresdner Fußballklub. DFC. Nee, die haben auch noch 'n Spitznamen. Unser Gast darf man nicht reden. Oh, der weiß auch nicht. Okay. Du googelst jetzt parallel. Anyway, wir gucken hier auf dieses Ding, weil wir sind auf der, 1 coolen Konferenz in Dresden, wo ich letztes Jahr auch schon war und wo ich heute dieses Mal begleitet wurde. Dynamo Dresden. Dynamo. Kennt man doch. Dynamo Dresden. Dynamo. Ah. Dynamo. Ah. Dynamo. Ah. Dynamo. Ah. Dynamo. Ah. Dynamo sieht man doch. Da leuchtet doch irgendwas. Wo's. Genau. Ja. Ja. Und wir sitzen hier in 'nem Gebäude von Vodafone. Vielen Dank an Vodafone, dass wir die diese Räumlichkeiten nutzen dürfen. Schaut dort Vodafone. Ich bin Dennis Becker, neben mir sitzt Dave Acker, Dave Krisczitzky. Moin. Genau und wir haben heute einen Gast eingeladen, der ist Senior Staff AI Engineer und bei Blue Yonder und das ist Martin Seeler. Herzlich willkommen, Martin. Vielen Dank für die Einladung. Schön, dass Du da bist. Da ist ja auch schon AI in deinem Titel, den Du den Du trägst. Das heißt, es hört sich so an, als würdest dich schon 'n bisschen tiefer damit auseinandersetzen und jetzt nicht grade erst an der an den Oberflächen kratzen, was das Ganze angeht. Aber erzähl uns gern 'n bisschen was über dich erst mal. Wer bist Du? Wo kommst Du her? Was hast Du so die letzten Jahre gemacht? Ja, also ich bin schon seit über 15 Jahren Softwareentwickler in allen möglichen Bereichen. Und jetzt seit etwas über einem Jahr bin ich bei 'ner Firma namens Blue Jonne. Die kannte ich übrigens auch vorher nicht, also falls es dir genauso geht, die machen Supply Chain Management, alles, was drum herum gehört, Warehouse Management, Transportation Management und von End to End wirklich alles mit dabei. Und meine Rolle ist Senior Star of Engineering, genau, im Bereich AI. Das heißt, ich bin in 'nem Team, das nennt sich AI Studio. Wir sind so das AI Frontier Lab bei Blue Hyundai und tatsächlich sind unsere Hauptaufgaben Post Training von Models. Wir bauen Agents, machen supervist Feintuning, Reinforcement Learning, wir bauen die ganze Infrastruktur. Also wir arbeiten wirklich an den neuesten Agents, die in den nächsten Jahren dann die autonome supply Chain bedienen sollen. Also ich würde schon sagen, cooles Zeug, womit man sich auseinandersetzt, macht auf jeden Fall eine Menge Spaß. Cool. 2 Fragen. Ey, i-Studio, wart ihr vor Google oder danach? Wann habt ihr das bitte benannt? Ja, kann ich dir nicht genau beantworten. Stand auf einmal so bei uns auf dem Team auf einmal drauf. Okay. Und Supply Chain denkt man ja irgendwie im Tech Bereich erst mal an wahrscheinlich was anderes, also ne, das irgendwie bedeutet irgendwie das Gleiche, aber es ist ja dann irgendwie Software und wird irgendwie reingepflanzt. Aber bei euch geht es Hardware, ne. Also das heißt Transport von Genau. Echten Waren von von echten Mitteln, also Sachen, die tatsächlich auf der Welt passieren und nicht nur in einzelnen Nullen ausgedrückt werden. Richtig. Also bei uns geht's darum, wenn mal wieder 'n Tanker im Suezkanal stecken bleibt, wie können wir die ganzen Güter von a nach b durchrauten? Wie hilft dir Software dabei? Bis hin zum Return Management, wenn der Kunde wieder Waren zurückgeben würde, genau. Okay. Wie definierst Du AI evals? Sehr breiter Begriff. Also das umfasst erst mal alles. Der Name selber ist ja nur die Kurzform von Evaluations, was erst mal alles mit dem Thema absichern, ja, bedeuten kann. Es geht eben darum, sicherzustellen, dass Du deine Core Value Chain sicherst, dass Du beweisen kannst, dass alles so funktioniert, wie Du dir das vorstellst. Auf welche Art und Weise das passiert, das ist abhängig vom Kontext, was Du gerade machen willst. Mhm. Und jetzt haben wir heute, also AI ja davorstehen und ich antizipiere irgendwie, eine der großen Schwierigkeiten ist vermutlich, dass es nicht mehr so deterministisch ist, wie vorher? Richtig. Also im klassischen Software Development Prozess hast Du den Vorteil, dass Du 'n Test schreiben kannst, 2 plus 2 ist 4. Sobald Propablistic Output in deiner Anwendung vorkommt, Du also, sagen wir mal, 'n hast wie 'n Large Language Modell, sehen die Dinge auf einmal etwas komplizierter aus. Du hast den Input 2 plus 2, aber jetzt hast Du den Fall, das LLM antwortet dir mit 4 oder das könnte eine 5 halluzinieren oder es schreibt dir 'n Gedicht über die Nummer 4. Mhm. Das heißt, Du hast auf einmal viel mehr Testfälle, die Du abdecken musst und das macht's komplizierter. Also mein Gefühl ist, wenn man sich damit beschäftigt und das, was ihr wahrscheinlich auch irgendwie schon implementiert, dann hat man sich ja auch schon 'n bisschen damit beschäftigt und versucht irgendwie, das Ganze unter unter einen Hut zu bringen. Was hast Du fürn Eindruck, wie das aktuell da draußen gehandelt wird, diese Problematik? Also ist ja, in der Regel sprechen wir jetzt erst mal davon, okay, AI wird in irgendwelchen Produkten eingesetzt, ne. Es ist jetzt nicht nur AI in unserem Entwicklungsprozess irgendwie mit drin, wo wir's ja selbst 'ner gewissen Art evaluieren oder Code Reviews machen oder keine Ahnung was, sondern wir haben auf einmal ein Stück Software, das wir auch ausliefern und das auch Endkunden irgendwie nutzen. Und da ist jetzt ein Part drin, der nicht so steuerbar ist und vermutlich ja auch irgendwie halt auch dieses Update Zeug 'n Riesenthema ist, oder? Also ich mein, das, keine Ahnung, es entwickelt sich irgendwie so schnell weiter, dass wir alle x Wochen irgendwie neue Modelle haben. Und da ist auch ein Part davon, dass man irgendwie da vorbereitet ist und das irgendwie abbilden kann? Genau, also wenn wir jetzt von EVIS sprechen und von dem Testen von solchen JennAI Produkten, dann geht's immer darum, Wege zu finden, zum einen die richtigen Tests zu schreiben. Also was genau sind denn die richtigen Tests? Das ist nämlich gar nicht so leicht zu beantworten. Und mithilfe von diesen Tests dann auch Ableitungen zu machen, zum Beispiel über die Qualität deiner Software, dem Ganzen eine Metrik zu vergeben auf Basis von deinen Tests. Und dann, ähnlich wie Du gesagt hast, es entwickelt sich alles rasend schnell weiter und immer wieder kommt ein neues Model raus. Du brauchst eine Art Test, der dir sagt, okay, es ist gut genug, wir können dieses neue Modell mit unserem Produkt ausliefern, das möglichst schnell bei. Kunden sind dann auch hyped, die wollen dann auch das neue Model sehen und in deinem Produkt mit verwenden. Und das gilt nicht nur für deine Kunden, sondern das gilt auch für deine eigenen Entwickler. Die sind genauso hyped, die lesen, oh, hier gibt's neue Technologien, das will ich ausprobieren. Hier gibt's neues Framework, das will ich ausprobieren. Also im besten Fall helfen dir diese EVIS dann auch noch, den richtigen Weg einzuschlagen. Auf was solltest Du dich denn aktuell auch konzentrieren? Also das sind alles Themen, wenn man jeweils richtig umsetzt und da gibt's sehr einfache Prozesse, auf die werden wir wahrscheinlich dann gleich noch eingehen. Mhm. Wenn man das richtig umsetzt, dann helfen dir diese Prozesse dabei, all diese Fragen mit einem Schlag zu beantworten. Wohingegen Du auf der anderen Seite häufig diesen sogenannten Vibe Check machst, kommt 'n neues Model raus, Du änderst vielleicht dein Model mal auf die neue Version. Mhm. Du lässt deine Anwendungen laufen und ja, schaust einfach nur, ja, fühlt sich gut an und wir releasen das und dann verschreckst Du aber den Kunden und das kostet dich im Endeffekt viel mehr Geld. Und ist dann zwangsweise aber damit auch verbunden, dass wir für diesen Schritt dann auch AI nutzen oder ist es da irgendwie besser, wenn man da wieder auf deterministische Systeme irgendwie zurückfällt oder geht das vielleicht gar nicht? Das geht sehr wohl. Es gibt 3 Blaue Frage. Es gibt 3 3 Wege, wie man das Ganze testet oder man kann das in 3 Kategorien unterteilen. Du kannst machen. Du kannst machen. Das, was Du angesprochen hast mit, Du lässt 'n lat Ranguage Model zum Beispiel dir dabei helfen bei der Evaluierung. Und der letzte Schritt, der wird häufig ganz außen vor gelassen, ist Human based Evaluations. Du setzt einfach einen Subject matter Expert an deinen Output, an deiner Anwendung, der das Ganze validiert. Alle Varianten haben ihre Vor- und Nachteile, ne. Also Du kannst, sagen wir mal, Du hast ein Report und Du willst die Qualität testen. Dann kannst Du mit sehr leicht einfache Dinge testen, zum Beispiel die Länge deines Reports. Wenn Du sicherstellen willst, dass dein Report nicht länger als 500 Zeichen ist, kannst Du das ganz einfach testen. Das kannst Du mit Code testen. Mhm. Das lässt sich verifizieren. Willst Du, dass deine Zahlen, die Du verwendest, in deinem Report immer mit Metriken versehen sind, kannst Du das mit 1 testen. Das kannst Du auch über Code lösen. Wenn Du allerdings auf Sachen eingehst, die subjektiv sind, kannst Du das nicht mehr über abbilden, nicht mehr über Code. Also kannst Du entweder über Model based Evaluations gehen oder über Human based Evaluations. Und dann kannst Du dort diese Tests durchführen. Oh, ist das irgendwie son Ding? Ich hab schon gefühlt hier und da gelesen, oder ich weiß nicht, ob das nur für das Code Review gilt, aber vielleicht geht das ja vielleicht als ähnlich. Nimmt man dann die gleichen Modelle oder ist es irgendwie besonders gut, andere zu nehmen, weil die dann nicht den, weiß ich nicht, gleichen haben? Oder ist das am Ende eigentlich egal, weil's 'n sehr anderer Schritt ist, sehr passiert? Eher letzteres. Also wenn Du dir die Aufgabe deiner Anwendungen anschaust, die nehmen wir jetzt als Beispiel 'n LLM. Mhm. Das bekommt irgendeine Aufgabe basierend auf diesem User Input, bestimmte Schritte zu absolvieren und dann einen Output zu generieren. Das ist im Vergleich zu einem Test ein ganz anderer Task, der dort absolviert werden muss. Und ist in der Regel kein Problem, wenn Du das gleiche Model für deine i-Wis verwendest als für deine normale Anwendung. Es gibt den Fall, der betrifft selten, aber kann vorkommen Modelle, dass, wenn ein Model sich selber Judged, dass es das erkennt, Mhm. Dass der Output von sich, von dem eigenen Model kommt Mhm. Und 'n gewissen Buyes aufweist. Mhm. Das ist aber auch sehr schwer rauszufinden und tritt sehr selten auf. Also ist nichts, was man von Anfang an jetzt mitberücksichtigen muss. 'N guter Ansatz ist immer, fang mit dem mächtigsten Model an, was dir zur Verfügung steht. Mhm. Und Du kannst später immer noch für Kosten optimieren und andere Modelle ausprobieren. Wichtig ist, dass das Model beim Evaluieren dem Human Judgement sehr nahekommt. Und da sind wir schon in dem Bereich, auch in der in dem Bereich, in dem Prozess, den wir dann gleich vielleicht genauer beschreiben, wie kann ich denn sicherstellen, dass so ein Model da tatsächlich dem übereinstimmt? Mhm. Wir hatten, hast Du eingeatmet? Ich hab eingeatmet. Okay. Wie vielleicht mal Sorry, nee, nee, nee, nee, nee, nee, nee, nee, nee, nee, nee, nee, nee, nee, nee, nee, nee, nee, nee, nee, nee, nee, nee, nee, nee, nee, nee, nee, nee, nee, nee, nee, nee richtig? Sondern das hat noch mal eine zusätzliche Abgrenzung. Also inwiefern würdest Du, also ich kann mir einfach anschauen so, oh, das Modell performt so und so, das ist so und so gut in diesen Cooding Tasks, das ist gut als LLM so. Also da gibt's ja verschiedene Benchmarks, wonach man sich richtig sag ich mal daher hergestellt. Aber wie würdest Du da die jeweils dann noch mal differenzieren? Also Benchmarks sind ein Teil von jeweils. Mhm. Denn was Benchmarks betrifft, Du hast eine Reproduzierbarkeit, Du testest immer wieder auf dem gleichen Input oder Du hast immer wieder die gleichen Aufgaben, die einfach von unterschiedlichen Modellen absolviert werden. Das ist ein Teil, eine Subkategorie von diesem Oberbegriff Ivals. Wenn wir jetzt von Ivice reden, reden wir ja häufig von LLM, 1 sehr konkreten Form von Ivice, wenn wir Lodge Language Modelle testen. Mhm. Das ist die Form, wo Du ein anderes Modell zum Testen verwendest. Also ich zeige einem Lodge Language Model, mein Output, schau dir doch mal meinen Report an und sag mir, ob hier ein gewisser Fehler vorliegt. Oder sag mir, ob ich auch schön in Gedichtform geantwortet habe. Mhm. Beispielsweise. Das ist nämlich sehr schwer, als Test einfach abzubilden. Das ist quasi unmöglich. Mhm. Also nehme ich mir einfach ein anderes Modell her und geborene ihm diese Aufgabe. Das sind diese, deswegen nennt man das LLM Message Judge. Mhm. Du lässt ein anderes Lodge Language Modell über deinen Output judgen, bewerten. Und wenn wir jetzt uns das so vorstellen, dass wir auch da ein Dataset haben und immer wieder die gleichen Inputs gegen unterschiedliche Modelle testen, dann könnte das unser ganz persönlicher Benchmark sein. Mhm. Wir können aber auch diesen dieses LAM so aufsetzen, dass wir Onlinemonitoring betreiben. Also jedes Mal, wenn ein User mit unserer Anwendung interagiert, wird dieser Judge ausgelöst und evaluiert diesen neuen Output. Jetzt hab ich kein vordefiniertes Dataset, was immer gleich bleibt, sondern stattdessen ein Judge, der jeden meiner Outputs graded. Genau, aber sind deswegen dieser Oberbegriff, nehm ich immer wieder den gleichen Input und teste das auf verschiedene Modelle, lasse ich immer wieder die gleichen Tests durchlaufen. Dann komme ich zu dieser Reproduzierbarkeit und lande bei Benchmarks. Mhm. Ja. Wir haben letztens, auch eigentlich 'n bisschen blauäugig, aber irgendwie in die gleiche Richtung. Also wir haben bei uns sone AI Week gehabt, wo das ganze Unternehmen einfach Zeit hatte, sich damit zu beschäftigen. Und dann sind wir auch viel son bisschen in die anderen Teams und Bereiche, die nicht nur Dev Entwicklungsteams waren, gekommen. Und da hab ich mit HR zusammen sonen, sagen wir mal, Prototypen, Prototypen zusammengehackt mit Cloud Code, wo es darum ging, dass wir für potenzielle Kandidat*innen, die wir im Active Sourcing uns angucken, die ist, ist, glaub ich, dann son bisschen impliziert passiert, was da eigentlich, also was 'n bisschen ein Teil davon ist, weil es gab dann so eine AI, die die Recherche gemacht hat und das Ganze irgendwie gesagt hat, hey, das sind die Sourcen. Und dann haben wir gesagt, na ja, irgendwie stell dir mal sicher, dass die auch relevant sind und dass da wirklich die Person geht. Und da kam dann er halt auch einfach und hat gesagt, ja okay, ich mach jetzt hier son Score hin. Und hat irgendwie schöne Graphen gemalt und gesagt, wie sicher es ist jetzt da, dass es sich die Person handelt und dass es irgendwie relevanter Inhalt ist dafür. Was irgendwie schon ein Teil dann ist oder da wo dann ein LLM irgendwie sagt hat, ja, was wie wie sicher bin ich mir denn? Und da war ich mir total unsicher, ist das, also kann man dem jetzt irgendwie trauen oder ist das auch irgendwie super random, was jetzt da rauskommt? Und die sagt, ja, ich bin mir mal zu 55 Prozent sicher, dass das richtig ist. Ja. Ja, also es ist sehr schwer. Das kommt davon, kommt darauf an, ob man das als Teil seiner Value Chain mit betrachtet, diese Bewertung von diesem anderen Modell oder ob man das Teil seines Testings betrachtet. Also wenn Du das jetzt als Teil der Core Anwendungen mitsiehst, würde ich eher sagen, handelt sich ein Multi Agent System, wo Du mehrere Large Language Models zusammenwendest, die unterschiedliche Aufgaben haben und die Sache zusammenbringen. Mhm. Wenn Du jetzt aber sagst, okay, das ist nur für uns zum Validieren, dann würde ich schon durchaus sagen, das geht schon in die Richtung Testing. Aber das ist ja das Schwierige, die Frage, was sind denn die die richtigen Tests? Mhm. Das ist sehr schwierig und ja, da da scheitern die meisten dran, denn es gibt sehr viele vordefinierte Ivals, die kann man einfach so installieren. Du hast dann ganz tolle Dashboards und Metriken, die dir dann angezeigt werden. Da steht dann eine Zahl drin, Helpfulness oder Trustwürphy und irgendeinen Wert zwischen 1 und 5. Mhm. So. Wenn ich jetzt eine Anwendung schreibe oder 'n Produktentwickler und meinem Boss sage, also wir haben hier ein Produkt und das hat einen Helpfunes Score von 4.2, dann guckt der mich mit runzelnder Stirn an und denkt wahrscheinlich, dass ich verrückt bin. Was was soll er dem das jetzt sagen? Also ist das jetzt gut genug? Können wir das jetzt auf 10000 Kunden losgehen lassen? Ja oder nein, das ist, das sagt dem gar nix, also Oder warum ist es nicht 5? Das wär eigentlich meine Frage. Alles sollte doch top sein. Genau. Ja, also das ist die schwierige Frage und darum ging's ja auch heute hier auf der Konferenz son bisschen in dem Talk. Wie schreibt man denn eigentlich richtig gute jeweils? Wie wie kommt man an den Punkt, dass man das versteht? Und wie nutzt man dann diese jeweils, die eben in in Metriken umzuwandeln? Und wie macht man's? Ja. Bam, guter Übergang. Sehr gute Frage. Es ist es ist eigentlich relativ leicht, es ist so leicht, dass es schon absurd ist, dass es nicht einfach jeder so macht, aber Spannend, okay. Ja, also ich bin wirklich auf die Antwort gespannt. Ja, also es ist tatsächlich so, es ist nicht mal was Neues, was sich jetzt jemand ausgedacht hat. Es ist etwas, das schon seit zig Jahren existiert, ein Begriff, der auch so trivial klingt, nennt sich Error Analysis. Und der besteht aus ganz wenigen Schritten. Schritt Nummer 1, Du sammelst dir Daten aus der aus deiner Anwendung. Du richtest irgend eine Art von Monitoring ein, erst mal die ganzen Daten zu sammeln. Jede Interaktion von deinem Customer mit deinem Produkt. Und Schritt Nummer 2 ist, Du schaust dir deine Daten tatsächlich einfach mal an. Also ganz, ganz trivial. Wer hätte das gedacht? Ja, bisher bisher wirklich einfach. Ja, Du schaust dir deine Daten einfach mal an. Und zwar in dem ersten Schritt nimmst Du die Rolle 1 Customers ein. Du bist im besten Fall jemand, wenn Du die Kommentare drinschreibst, der diese Produktidee hat, der bewerten kann, was was genau heißt denn gut aus Customer Sicht? Und Du nimmst seine Rolle ein und schaust dir deinen Output einfach mal an. Und ich kann ja mal ein Beispiel bringen, bei uns bei Blöhrender, wir haben natürlich viele Projekte, 1 ist auch so ein Report für Warehouse Manager, wo wir alle möglichen und Probleme aufdecken. Und unser Report enthielt dann Daten zum Beispiel zu Verspätungen bei Shipments, Late Shipments. Mhm. Der erste Satz war gleich, wir haben 100 Prozent Late Shipments und das basiert auf 0 Late Shipments, so. Also wir haben keine Late Shipments. Es gibt keine, aber wir reporten irgendwie 100 Prozent. Das macht natürlich keinen Sinn. So, also hinterlässt man einfach nur den Kommentar, dass es hier eine Diskrepanz gibt. Ganz, ganz trivial. Einfach nur runterschreiben, mehr nix. In dem gleichen Report haben wir auch noch davon gesprochen, von Aber wir hatten ja gar keine Late Shipments. Also das ist totale Übertreibung. Dann mach ich mir genauso 'n Kommentar. Hier wurde übertrieben und ohne eine konkrete Zahl, das ist ja auch noch mit dabei, was bedeutet denn in dem Kontext, Mhm. Auf einen Missstand hingewiesen. Noch viel schlimmer, wir haben dann bewertet, wie gibt es Probleme mit den Leuten, die im Warehouse arbeiten? Und dann standen da so Sachen drin wie, Low Performers, das ist halt kritisch. Also wenn ich ein Produkt rausgebe an Tausende von Kunden mit jeweils 10000 Warehouse Mitarbeitern und ich nenn die alle Low Performer, dann hab ich wahrscheinlich am nächsten Tag von HR einen Brief in meinem Postfach, dass die mich zum Gespräch einladen. Also hinterlasse ich auch da einen Kommentar, dass wir hier HR sensitive Sprache in unseren Reports verwenden. Ich schreib also alle diese Kommentare runter. Ich geh durch und am besten annotier ich nur diesen ersten Upstreamfehler. Denn alle Sachen, die ich danach entdecke, haben wahrscheinlich mit dem Anfang dem Problem zu tun, ab wo der erste Drift Mhm. Abgegangen ist. Und das reicht aus tatsächlich, man könnte jetzt meinen, oh, da muss ich ja Tausende mir solcher Traces angucken, aber nein. Das war mein erster Gedanke, ja. Tatsächlich musst Du das nicht. Du kannst sehr, sehr leicht anfangen mit ganz wenigen, beispielsweise 20 Traces. Mhm. Du gehst einfach dadurch, das dauert vielleicht 2 Stunden, 3 Stunden und Du hast mindestens 100 Kommentare gesammelt. Und zwar Kommentare, die perfekt auf dein Produkt zugeschnitten sind, die Fehler in deiner Anwendung aufzeigen. Denn es ist sehr leicht für jemanden im Produktmanagement sich zu überlegen, was möchte ich denn an Output sehen? Aber auf der anderen Seite ist es sehr schwer, im Vornherein darüber nachzudenken, was soll es denn nicht sagen? Was möchte ich denn nicht sehen? Mhm. Und das sehe ich natürlich nur, wenn ich mir die Daten tatsächlich angucke. Also wir haben dann, insgesamt haben wir 8, 87 Reports uns angeguckt, sind da durchgegangen und hatten etwas über 160 Kommentare zusammengesammelt. Dann haben wir aufgehört. Warum haben wir aufgehört? Wir haben einen Punkt erreicht, den nennt man Theoratical Saturation, theoretische Sättigung. Mhm. Das ist der Punkt, an dem Du feststellst, hm, ich schreib zwar Kommentare, aber das hab ich alles schon mal hingeschrieben. Irgendwie find ich grade nix Neues. Hier ist eigentlich Schluss. Du kannst hier aufhören. Ab hier kostet dich der Prozess einfach nur noch Geld Ja. Und Du hast sowieso schon sehr viel gefunden. Okay, also das waren die 2 Schritte. Wir müssen erst mal sicherstellen, dass wir die Traces sammeln, diese Logs von unserer Anwendung und dann gehen wir da durch und kommentieren das. So, Schritt 3 ist, Du gruppierst diese ganzen Fehler. Und Du sammelst alle, dafür kannst Du dann tatsächlich wieder ein LLM nehmen. Du könntest alle Kommentare nehmen, die 'n Chat gbt reinschmeißen und sagen, bitte stell mir mal 'n paar Gruppen. Oder Du gehst technischer ran und machst das auf Basis von Keywords oder Embeddings, die zu clustern, kannst Du machen, wie Du das für richtig empfindest. Und in unserem Fall gibt es mehrere HR sensitive Kommentare und wir haben das zusammengefasst zu 1 Gruppe, die umschreibt dieses Problem sehr gut. Und das ist auf einmal ein sehr guter für unsere Anwendung im Vergleich zu Helpfulness. Mhm. Hundertprozentig. Ja, wenn ich das meinem Boss zeige und dem sage, unser ist dies und das, ist das viel, viel hilfreicher. Diese 168 Kommentare, die wir hatten, haben wir auf 19 Kategorien runtergebrochen, also 19 sogenannte sind dabei rausgekommen. Das sind die Zustände deiner Anwendungen, die Du eigentlich nicht sehen möchtest. Also ja, Du kommst am Ende auf Tests, die genau das testen, was deine Ahnung nicht mehr machen soll. Und das kannst Du auch immer wieder machen. Es ist ausreichend, dass Du damit einmal anfängst und das alles aufsetzt, aber es wird der Punkt kommen, dass deine Nutzer anders mit deiner Anwendung interagieren. Und bestes Beispiel ist ChatGPT. Wenn Du mal daran denkst, als ChatGPT rauskam, was hat jeder gemacht? Der hat sich irgendwelche lustigen Gedichte erzeugen lassen. Schreib mir mal ein Gedicht über Softwareentwicklung oder was weiß ich. Heutzutage macht das niemand mehr, das ist langweilig geworden, aber am Anfang haben die Nutzer so mit ChatGPT interagiert. Und das nennt man User Drift, die Nutzer verändern, wie sie mit deiner Anwendungen interagieren. Wenn Du das feststellst, dann kannst Du diesen Prozess wiederholen. Aber erst mal ist das tatsächlich eine Arbeit und Du bist sehr gut abgesichert erst mal. Also unsere 19, die die bleiben ja echt, die bleiben ja auch relevant. In 2 Monaten sag ich ja nicht auf einmal, ist nicht mehr relevant für unsere Anwendung. Das heißt, das kann ich kosteneffizient einfach so umsetzen. Und was Du dann machst, ist, Du Du Du bildest das als Test ab, der die ganze Zeit dann deine Outputs graded. Also da sind wir wieder bei diesem LLM Message Judge. Mhm. Da gibt es ja alle mögliche Software. Wir benutzen zum Beispiel Langviewes und dann schreibst Du da eine prompt. Und in diese prompt schreibst Du rein, hier, das ist ein Report und bitte teste, ob sich hier drin befindet, ja oder nein. Und in diesen in dieser Anfrage kannst Du diesem anderen Luch Language Modell auch Beispiele reingeben, nämlich die Beispiele, die Du in deinen Kommentaren gefunden hast. Ist also ideal. Wir können also sagen, wenn wir hier von Low Performern reden, ist das schlecht. Können wir dem direkt so als Beispiel geben. Mhm. Und dann schmeißen wir dort den Report rein. Und was rauskommt, ist ein, ja, was was kommt eigentlich raus? Das ist schon das nächste Thema. Häufig wird ein Wert zwischen 1 und 5 erwartet, ne. Wie bei diesen Helphoenes. Ja. 1 bis 5. Wenn ich dich jetzt fragen würde, ist das 3.7 oder 8 8.5? Ich hab keine Ahnung. Dann würdest Du dir wahrscheinlich auch die Frage stellen, ja, wo ist denn der Unterschied? So. Und das zeigt schon das Problem mit solchen sogenannten Leicad Skalen. Die sind in der Theorie ganz nett, weil Du dieses Gefühl hast, dass Du eine Metrik hast, die sich verbessert, aber die sagt dir eigentlich nix. Ja. Wobei, ich würd sagen, komparativ schon. Also wenn ich weiß, so vorher wurd das auf 8 geschätzt und jetzt hab ich bei Erinnerung gemacht, also auf 4, dann würd ich sagen dann, okay, also ich kann nicht, ich kann mit dem Wert per se nichts anfangen, aber ich weiß es Genau. Scheint irgendwie weniger justmental zu sein, was vielleicht gut ist. Ja. Das stimmt. Auf der anderen Seite, wenn Du einen Wert zwischen, sagen wir mal, 0 und 5 hast, dann passiert was Spannendes. Sagen wir mal, wir haben 100 Outputs oder 100 Reports und wir lassen ein LLM as Judge, unsere 100 Reports grainen nach Judgemental language. 0 bedeutet, absolut kein Judgemental language enthalten. 5 ist, wir beschimpfen Ja. Unsere Worker hier von vorne wissen. Ja. Und wenn Du jetzt über die 100 Reports einen Durchschnittswert von 0 hättest, veremdest Du das okay? 0? Also keiner unserer Reports hat Judment Language. Wenn Du jetzt mein Boss wärst und ich frag dich, können wir das Produkt so rauslassen, würdest Du sagen Das, also es ist schon als Fangfrage formuliert, so. Ich merk schon, ich laufe eine Falle, aber ich würd erst mal sagen, weil ich geh drauf ein, ja klar. Nur, wir wollen ja gar keine Challenge an Englisch haben, ne. Perfekt, perfekt. Können wir releasen können wir releasen. Was ist denn, wenn unser Durchschnittswert 1 ist? Risikoabwägung. Ich will, da würd ich auch noch sagen, das können die Leute ab. Okay. Und bei 2? Ich glaub, ich find bei 5 sogar, ja, sag ich, ich will 'n schlechtes Beispiel dafür, glaub ich. Aber Wär stimmt, aber genau, irgendwann wird man sich, glaub ich, selbst erwarten, 'n Wert zu schreiben, so was ist noch hinnehmbar? Genau. Also Du läufst in die Falle, dass Du aus diesem Problem von, bewerte das mal zwischen 1 bis 5 oder 0 bis 5. Du machst sowieso wieder 'n binäres Problem da draus, denn Du musst an irgend 'nem Punkt sagen, hier ist der Cut off. Ab diesem Wert fühlen wir uns nicht mehr gut, das zu releasen. Ja, was was ich auch erst mal sagen würde, was ich okay finde. Also wir arbeiten ja auch schon seit Jahren mit irgendwie statistischen Modellen und sagen so, hey, das ist signifikant, so mit 1 Wahrscheinlichkeit von 95 Prozent, ne, also Unschärfe p gleich 0.05 so. Machen wir ja häufiger so. Und dann denk ich mir so, er ist wahrscheinlich dann in Ordnung und wenn man das dann da festlegt, fänd ich's, glaub ich, auch erst mal irgendwie okay. Aber jetzt nehmen wir mal unsere neunzehnten Failormotes. Mhm. Und für jeden Failormode müssten wir jetzt definieren, oh, bei George language ist 3.82 ist dann der Schwellenpunkt, an dem wir sagen, oh, hier geht's nicht weiter. Wenn wir aber sowieso wissen, wir machen 'n binäres Problem da draus Oh. Und es fällt uns ja persönlich auch schwer zu bewerten, enthält dieser Report Judgement Language jetzt einen Wert von 3.84 0.2, ja? Menschen tendieren dazu, sich irgendwo in 'ner sicheren Mitte anzusiedeln. Die wollen nie konkret werden. Mhm. Das Gleiche passiert bei Dutch Language Modellen auch. Mhm. Und in dem Moment, wo Du ein binäres Problem da draus machst, wird die Aufgabe leichter. Mhm. Das heißt, Du fragst das l m jetzt nicht mehr zwischen für einen Wert zwischen 0 und 5, sondern gib mir 1 oder true zurück, wenn Judge Wandler Language enthalten ist und 0, wenn es nicht enthalten ist. Einfache binäre Entscheidung. Ja. Das macht die Sache leichter. Und dann kannst Du auf 100 Reports gesehen eine Metrik berechnen, nämlich die. Mhm. Oder andersrum, es ist ja eher die, Genau. Wie viel Prozent meiner enthält. Und das bringt uns auch schon zu dem Schritt, warum das Ganze so so nützlich ist, denn wir hatten ja gesagt, es ist schwer, erst mal rauszufinden, was sind denn gute E-Mails? Das haben wir rausgefunden, indem wir uns tatsächlich unsere Outputs angeguckt haben. Das heißt, wir haben jetzt 19 sehr hochspezifische E-Mails für unser Produkt rauskristallisiert. Und dadurch, dass wir jetzt auch diesen Mechanismus haben, der die immer wieder bewertet, können wir jetzt uns ein Datenset nehmen und 19, unsere 19 Kategorien. Und was weiß ich, wir nehmen uns 100 Reports und schauen einfach mal, welcher Fehler tritt denn am häufigsten auf? Du kannst dann also 'n schönes Chart dir generieren lassen auf 100 Reports, welcher Fehler kommt denn am häufigsten vor und wie oft? Das gibt dir eine schöne Sichtbarkeit. Mhm. Es macht die Fehler sichtbar und hilft dir, 'n Gefühl dafür zu bekommen, welche Fehler wie oft vorkommen. Und dir als Manager, falls Du dann da derjenige bist, der entscheiden muss, woran arbeiten wir denn jetzt oder woran arbeiten wir denn als Nächstes, hilft das, die Sachen zu priorisieren. Wir könnten Judgement language jetzt einen Wert zuweisen, indem wir sagen, was hat das für einen Impact für uns, für den Customer? Was kostet, was bedeutet das kostentechnisch für uns? Es kann Fehler geben, die sind sehr selten, aber die sind so schlimm, wenn das passiert, dann verlassen uns die Kunden. Mhm. Ja? Das sind also Entscheidungen, die Du jetzt auf einmal datengestützt treffen kannst. Weil Du siehst, oh, auf 1000 Reports gesehen, ist dieser Fehler ja nur zweimal aufgetreten. Aber wenn der passiert, dann rollen hier Köpfe. Mhm. Andere Sachen sind vielleicht sogar in 80 Prozent der Fälle. Sagen wir mal, die Metrik fehlt. Mhm. Da würde dir aber vielleicht jetzt kein Customer unbedingt abspringen. Also Mhm. Kannst Du abwägen. Da gibt's immer diesen Vergleich mit dem Spülbecken. Wenn Du jetzt abwaschen musst, nimmst Du den großen Topf zuerst, erst mal Platz zu machen oder fängst Du einmal von oben an und gehst zu diesem Weg da durch. Genau und das bringt da ja auch gleichzeitig dann diese Metrik und jetzt kommst Du in dieses schöne Datafly Wheel rein. Das ist dieser Prozess, dass Du jetzt immer wieder iterieren kannst. Wir können jetzt unsere Anwendungen testen. Wir haben unsere Stats, wie oft welcher Fehler vorkommt. Wir können priorisieren, welche Fehler sind die, die als Nächstes angehen müssen. Mhm. Und mit dieser Information können wir jetzt an unsere Entwickler rangehen und sagen, hey, wir müssen Judgemental Language zum Beispiel im nächsten Release angehen. Jetzt hat der Entwickler 'n ganz konkretes Ziel. Mhm. Ja, der der kann jetzt sein neues Rax System ausprobieren, der kann jetzt sein neues Framework ausprobieren, der kann die prompt umschreiben, Mhm. Worauf der Bock hat. Ja. Und dann lässt er einfach die jeweils noch mal durchlaufen und schaut, welchen Impact hatte dann meine Veränderung? Ja. Er kann eine ganz konkrete Zahl seinen Manager geben und der kann dann entscheiden, ja, das funktioniert. Und Du kannst diesen Prozess von diesem gruppieren, kannst Du sogar noch mal eine Stufe wiederholen. Mhm. Denn deine Fehler, die kannst Du auch noch mal gruppieren. Und in unserem Beispiel, wir hatten jetzt diese dieses Beispiel mit der fehlenden Metrik bei 1 Zahl. Wir hatten dieses Beispiel mit Judgemental Language. Beide Probleme sind in dieser Kategorie von der Darstellung oder wie ich das Ganze reporte, wir haben's genannt, einzugliedern. Und in dem Moment baut man eine Taxonomie von Fehlern. Und diese 19 Fehleremotes haben wir in 4 Toplevel Kategorien klassifiziert. Das war Kontext clarity, Präsentational Quality. Und ich kann jetzt nicht nur sagen, im nächsten Release greifen wir Judgement Language an, sondern der nächste Release konzentriert sich komplett auf präsentational Quality zum Beispiel, ja. Und das gibt natürlich auch Entscheidern die Möglichkeit, jetzt genau zu planen, was, woran wollen wir arbeiten? Die können das schon ans Marketingeam geben. Release Nummer 2 wird sich darum kümmern, Release Nummer 3 darum und so weiter. Also es gibt ja Planbarkeit, es gibt ja konkrete Zahlen. Und vor allem, genau, diese Quantifizierbarkeit find ich irgendwie interessant. Was in meinem Kopf fürn Konstrukt entstanden ist, ist irgendwie so, Du hast gesagt, Du hast diese 19 E-Bikes oder dann diese 4 Hauptkategorien und die dann einfach irgendwie so son Teil von soner riesigen Formel sind, so und die gewichtest Du dann halt einfach anders. So und dann am Ende kommt irgendwie 'n Wert raus so und sagst so, okay, wenn's diesen Wert überschreitet, dann können wir nicht releasen. Wenn's drunter ist, dann haben wir das erreicht, was wir erreichen wollen. Also irgendwie find ich's so schön, dass da aus so sehr viel nur Wahrscheinlichkeit irgendwie dann am Ende doch noch was zahlenmäßig rauszuhauen. Ja, und das hilft dir natürlich auch. Wir hatten ja am Anfang da drüber gesprochen, diese Traces, diese, ja, Logs von User Input bis zu deinem Output, dass man die erst mal und sammelt. Diese Software, die man dafür einsetzt, da gibt's ja viele am Markt, nehmen wir mal Langfuse als Beispiel, hilft dir ja auch dabei zu tracken, wie viele Token hat denn dieser ganze gekostet? Mhm. Und auf Basis von dem Modell, was Du verwendest, also was hat denn das auch in Dollar oder in Euro gekostet, der ganze Roundtrip? Und die Latenz wird gemessen. Was wie lange hat denn der Requiz gedauert? Und wenn Du diese Sachen jetzt kombinierst, Du hast dein deinen Errorwert, ja, die Qualität deiner Anwendung. Du hast die Latenz, Du weißt, wie lange dein Request gedauert hat und Du weißt, was dein gekostet hat. Jetzt kannst Du auf einmal ganz konkrete Aussagen treffen. Mhm. Beispielsweise bei uns kamen dann raus, es war damals, wir hatten noch GPT 4 o am Laufen, war irgendwann Anfang letzten Jahres. Und dann kam GPT 4 Punkt 1 raus und auch das Nanomodell. Und ich weiß noch, dass wir dann die jeweils haben laufen lassen. Mhm. Und ich konnte dann zu meinem Manager sagen, okay, GPT 4 Punkt 1 kam gestern Abend raus. Wir haben 6.2 Prozent mehr Fehler in Kontext clarity, aber wir haben 37 Prozent schnelleren Output und wir haben 96 Prozent weniger Kosten bei der Royal Quiz. Ja. So und das sind Zahlen, die kann ich jetzt meinem Boss vorlegen und der kann dann Entscheidungen treffen. Das heißt, wir können innerhalb von, was weiß ich, 24 Stunden nach Model Release entscheiden, ist es gut genug, dieses Model zu releasen, ja oder nein? Und vorher war das eben dieses Viepetriebene, ich, ja? Probier das einfach mal aus, ja? Das ist natürlich jetzt eine gewisse Sicherheit, die man dadurch erreicht hat. Alles nur, weil Du dir mal deinen Output angeguckt hast. Ja. Ja, also vor allem, also den Aspekt find ich halt besonders gut, weil ich glaub, das ist auch sone Entscheidung, die bei uns oft irgendwie so im Raum ist, wo wir uns nie sicher sind so, hey, okay, nutzen wir jetzt das Modell, gehen wir jetzt auf Gemini, nutzen wir, ist Claude so wirklich das Wahre. Sind diese ganzen Models von Mysteral irgendwie ganz geil und wir nie wissen, also ist alles sehr nach Gefühl. Dann wirklich zu sagen so, hey, okay, es ist vielleicht minimal schlechter in dem Bereich, aber es dafür deutlich schneller in dem Output und kostet deutlich weniger Geld. Ich glaub, diese Sichtbarkeit dadurch zu haben durch jeweils, also find ich, ist schon 'n ganz guter Vorteil auf jeden Fall. Ja, wir haben einen Punkt ausgelassen. Wir haben vorhin drüber gesprochen, wie sehr kann ich denn dem Model trauen, dass es auch wirklich richtig evaluiert. Nehmen wir mal jetzt 100 Reports. Wir haben ja identifiziert, ist ein, den der für unsere Anwendungen relevant ist. Mhm. Bleibt mal bei dem Beispiel. Dann kann ich natürlich einen Experten aus meiner Domäne an diese 100 Reports ransetzen und dem die vielleicht nicht ganz schöne, aber wichtige Aufgabe geben, geh mal bitte durch diese 100 Reports durch und sag mir, ob das Judgement Language enthält, ja oder nein. Das heißt, ich habe ein human Label für ein gewisses Subset. Und das ist mein Golden Dataset für für diesen Fehler Mode. Und dann kann ich diese prompt schreiben für diesen dieses und eben die gleichen 100 Reports zeigen. Mhm. Und was dann relevant ist, ist wieder dieser Data Science getriebene Ansatz. Ich schau mir einfach an in 'ner Matrix, wie viel Übereinstimmung ist da? Also ich schau mir an, wie oft wurde wirklich vom Human das Label Judgement a language vergeben und wie oft wurde das Label vom Agent oder vom LLM vergeben? Und ist es auch an den gleichen, ja? Also man sagt dann, man hat dann diese True Positives, True Negatives und dann gibt's eben die Ausreißer, wo Du dann ja, Unstimmigkeiten hast. Und deswegen ist es so wichtig, dass Du da einmal ein Human auch mit ran setzt, denn nur so kannst Du dir dann auch sicher sein, dass dieses LLM wirklich mit deinem Human Alignment übereinstimmt, dass der wirklich genau das testet, testest, wie Du das auch testen würdest. Also dafür ist dieser Schritt dann relevant. Wir mussten irgendwie, ich würde relativ wenig Nachfragen stellen. Das, also Du hast es einfach sehr 'N Selbstläufer. Du hast es einfach sehr, sehr klar und stringent uns uns dargestellt. Ja. Ich Das Einzige Ja, nee, nee, trotzdem beide Fragen. Ja, wir beide Fragen. Nee, frag Du zuerst. Du hast länger nichts gesagt. Ich muss darauf achten. Die, gibt es dafür ein, gibt es eine technische Empfehlung, weil Du hast irgendwas gesagt, was machst Du mit Lang, wie hieß das? Langfuse. Langfuse. Ja, das ist eine Software, die dir hilft, diese Traces zu sammeln. Also ist eine Monitoringanwendung für Jenny Eye Anwendungen. Okay, cool. Weil das wär nur so das Ding gewesen, okay, wie wie hieß son bisschen die technischen Vampire? Weil ich glaub grundsätzlich, dass, ne, die Idee kam, glaub ich, sehr klar rüber. Und dann wär ja jetzt sone der nächste Schritt, okay, wie kriegt man das konkret irgendwie bei sich eingebaut, Ja. Was das ist. Und dann ist es zumindest ja vielleicht eine Lösung, die man sich mal angucken kann. Genau, oder Reintest, ich will mich da gar nicht festlegen. Phoenix ist noch eine. Mhm. Comptfu. Es gibt viele, viele Anbieter. Ich bin da sehr. Denn alle alle funktionieren sehr gut und bieten auch sehr viel Funktionalität. Damit anzufangen mit diesem ganzen Prozess, könntest Du dir genauso gut einfach alle in irgendeine txt datei loggen und dann da durchgehen. Also der Prozess selber, der bedarf ja keiner Software, aber dieser Prozess wird durch sone Software noch einfacher. Unterstützt, ja. Genau. Ja, aber es ist auf jeden Fall sehr einfach und man erreicht sehr viel dadurch. Ich hatte vorhin in in dem Talk jemanden, der hat mir dann tatsächlich die Frage gestellt, oh, das ist aber, das klingt aber sehr teuer. Da muss ich ja jemanden da hinsetzen, der da durchgeht. Und der wollte mir dann wirklich sagen, dass dieser Prozess für ihn irgendwie nicht so gut ist, weil er sehr teuer ist. Und dann hab ich ihm vorgerechnet, ja, pass mal auf. Also wenn ich jetzt jemand, ich sag mal, einen Tag Set-up von Langviews Und ich setz einen Tag jemanden hin, der da 100 Traces bei uns durchgeht und sich das Ganze mal anschaut. So, dann hab ich da 2 Mann Tage investiert und dann schreib ich mir da 'n paar Prompts. Was wäre denn gewesen, wenn ich jetzt diese Software released hätte, wo wir unsere Leute Low Performer nennen? So, das wäre jetzt an 10000 Kunden ausgeliefert worden. Mhm. Davon hätten sich dann 500 verabschiedet und das hätte uns dann 20000000 gekostet. Ja. Ich denke, hier ist schon klar, die Kosten, die amortisieren sich bei diesem Prozess, bei diesem so einfachen Prozess sehr schnell. Also Kosten sind auf jeden Fall gar kein Gegenargument. Und das Spannende ist auch, das ist auch ein Prozess, der macht den Leuten tatsächlich Spaß. Mhm. Denn wenn man so durchgeht und sich mal durchliest, was ist denn so der Output? Man erkennt manchmal so lustige Dinge auch oder den greift sich an den Kopf, ja? Man sieht's ja auch immer wieder in den in den in Reddit. Selbst die großen Player, Google, Amazon, selbst die haben ja nicht fehlerlose Software, weil wir reden von strastischen Systemen. Ich musste so lachen bei diesem Seepferdchen Emoji Hast Du gesagt, Alter, ich konnt nicht mehr volllachen, das war so lustig. Das war so lustig, oh Gott. Aber es geht natürlich auch manchmal in eine richtig blöde Richtung. Wir hatten jetzt bei Google ja auch diesen KI Mode, wo Du Fragen stellen kannst und manchmal ist dort ja wirklich sehr komischer Output, beispielsweise wurde ja dort empfohlen, dass Du Kleber auf die Pizza machen sollst, weil es tatsächlich einen Rat, wo das so beschrieben wurde. Ja. Oder Google empfiehlt, dass es sinnvoll sein könnte, einen kleinen Stein pro Tag zu essen. So. Wenn Du jetzt Engineer bei Google bist und Du kriegst die Aufgabe, schreib doch mal Tests für den neuen KI Mode, Ja. Wäre das wahrscheinlich kein Test, der dir am Anfang in den Sinn gekommen wäre. Aber dadurch, dass Du dir tatsächlich mal deine Daten anguckst, Ja. Ja, schaffst Du das. Ja, und vielleicht den Bogen auch zu machen zu dem Bereich, wo wir arbeiten, so Model Training. Mhm. Wenn Du diese jeweils hast, gibt dir das natürlich aus Produktsicht Sicherheit, ja, und auch diese Metrik. Was Du aber auch machen kannst, ist diese jeweils dann später fürs Modeltraining, fürs Steering einzusetzen. Also wenn man Judgement Language jetzt als Evaluator hat, kann ich diesen Evaluator benutzen, Outputs zu validieren, die mir ein Model generiert. Ich judge das und nutze das, das wieder als Feedback beim Reinforcement Learning reinzubringen und das Model dann automatisiert in die richtige Richtung trainieren zu lassen. Also es hilft mir nicht nur auf Produktebene, sondern eben dann auch später beim beim Training und überall und ja. Also es ist wirklich eine sehr, sehr nützliche Sache, kann ich nur empfehlen. Mhm. Hört sich sinnvoll an. Ja. Vielleicht 'n Aspekt, weil weil Du es häufiger erwähnt hast, diese also sehr spezifischen, auf euer Produkt zugeschnittenen jeweils, ne, die 19 Stück, die ihr dort dann irgendwie identifiziert habt und dann auch dann klassifiziert habt, ist, also wie Du richtig gesagt hast, identifiziert habt und dann auch dann klassifiziert habt, ist, also wie Du richtig gesagt hast, ist es auf jeden Fall irgendwie 'n Prozess, 'n Aufwand, den man machen muss und der sich so nicht 1 zu 1 auf andere Produkte übertragen lässt. Und da ist son bisschen die Frage, vielleicht gibt's da schon etwas so, was vielleicht 'n bisschen generischer ist, so Schritt 1? Oder sagst Du, nee, man wird dabei bleiben müssen, sich wirklich sein Produkt anzuschauen, sich wirklich damit auseinanderzusetzen, gucken, was sind denn unsere unsere eigenen Benchmarks? Was ist uns wichtig? Und dann halt quasi immer auf eine sehr hochspezifische Lösung am Ende zu kommen. Ja, also ich, ja, es gibt solche fertigen Lösungen, aber ich bin kein Freund von. Der einzige Vorteil, den man davon vielleicht haben könnte, wäre es zu verwenden, dein initiales Dataset zu erstellen. Wir haben ja gesagt, wir gucken uns die ersten 20 Trazens an, das reicht in der Regel aus, schon mal 'n Gefühl dafür zu bekommen. Wenn Du jetzt aber Tausende von Travis hast, ja, welche 20 nimmst Du denn da? Das muss man ja auch erst mal machen. Das könntest Du jetzt natürlich random sampeln oder Du sagst, Du nimmst dir die 500 längsten. Oder Du nimmst eben solche generischen Ivals wie Helpfulness, die es eben fertig am Markt gibt, Mhm. Die aber sehr, sehr generisch sind Ja, okay. Und würdest dann sagen, okay, alle, die unter 'nem gewissen Score sind, das sind die ersten 20, die ich mir anschaue. Für so was kann's sinnvoll sein. Okay. Aber ansonsten macht es einfach Sinn, dass Du hochspezifisch auf dein auf dein Produkt diese anwendest. Mhm. Jetzt auf sonen Kontext wie Blue Yonder, ein Riesen Enterprise Unternehmen gesehen, es gibt durchaus den Fall, dass jeweils für verschiedene Projekte relevant sind und nicht nur produktspezifisch sind. Jetzt in das Beispiel von diesem Report, das ließe sich bestimmt auch auf andere Dinge im gesamten Unternehmen anwenden, ja. Denn überall dort, wo man Customer Facing irgendwie unterwegs ist, Ja. Möchte man ja kein judgmental language haben. Aber es gibt auch andere Dinge, beispielsweise nehmen wir mal den Punkt von Models. Gerade jetzt im internationalen Supply Chain Kontext kann es durchaus Sinn machen, generische, ich sag jetzt generisch in Anführungszeichen, Ivals zu schreiben, die zum Beispiel sicherstellen, dass man keinen geografischen Bias mit drin hat. Mhm. Wenn ich jetzt Kunde von so 1 Software bin und ich frag einen Agent, wie könnten wir denn die, oder welches Land wäre denn das Beste, meinen Truck da durchzuschicken? Dann will ich keinen geografischen Bias haben, weil der Präsident irgendwie blöde Sachen tweetet, sondern ich will möglichst unvoreingenommen an die Sache rangehen. Aha. Das sind Dinge, die sind natürlich für alle Projekte relevant. Also wenn man so eine Art Dashboard baut mit solchen generischen Sachen, kann es schon Sinn machen, dass man das auf mehrere Sachen ausweitet, aber das ist eher die Ausnahme und sehr schwer zu finden. Cool. Können wir noch mal ganz kurz auf gehen? Jo. Sind? Nicht auf Record off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off off sind. Viel Feintuning von Modellen und irgendwie da eigene Sachen anzupassen. Ist das noch, also ist das noch 'n Ding, hört sich irgendwie vielleicht falsch an, aber also Oh, ich frag das immer mal genau, also macht das noch Sinn oder entwickeln wir uns irgendwie, sind die so groß und ist es dann einen Kostenfaktor, weil man kleinere Modelle nehmen kann oder ist es wirklich dann, weil man diesen Output aus den großen Modellen nicht rausbekommt? Glaubst Du, es wird immer noch eine, also wird es lange längerfristig in Zukunft haben? Wie ist son bisschen der Take darauf, weil ihr ja anscheinend viel in dem Bereich macht, was jetzt auch nicht jeder macht? Also es gibt diesen schönen Spruch, Models come and go and ivals are to stay, noch mal beim Beim Thema bleiben. Ja. Du kannst halt immer wieder neue Models testen. Mhm. Und die ivals schreibst Du in der Regel nur einmal. Mhm. Ich denke, es wird noch sehr, sehr lange Sinn machen, dass man eigene Model in die richtige Richtung drückt. Mhm. Auf welche Art und Weise, ob das jetzt superweist Feintuning ist oder Reinforcement Learning. Das hängt immer vom Anwendungsfall ab. Also der Hauptunterschied zwischen diesen beiden Varianten ist ja der, beim supervice Feintuning hast Du ein Groundthrof Dataset und Du bringst das dem Model bei. Das heißt, Du willst dort die Fehlerrate minimieren, dass das von dem von den Vorgaben abweicht. Mhm. Beim Reinforcement Learning ist es genau andersrum, Du hast diesen Grader, der dir 'n gewissen Reward gibt und versuchst diesen Reward zu maximieren. Wenn man das jetzt in einfacher Sprache ausdrücken wollte, könnte man das Beispiel von Omas Rezept nehmen. Wenn Du jetzt ein Model beibringen willst, wie Omas leckere Lasagne hergestellt wird, dann wäre der Vergleich beim Super Vice Feintuning, dass Du jeden einzelnen Schritt vom Model kontrollierst, dass es auch genau das macht, was Oma im Rezept hier gemacht hat. Und immer wenn's leicht abweicht, gibst Du 'n gewissen Fehlerwert. Beim Reanforcement Learning sagst Du, das muss genau so schmecken oder Du Du bewertest den Geschmack. Mhm. Und Du lässt aber mittendrin das Model machen, was es will und Du versuchst es gar nicht einzuschränken. Wenn der am Ende mit der Idee rauskommt, der schmeißt alles in die Luft und es landet Random Zeug in der Lasagne drin und Du schmeißt das zu 'ner Random Zeug in den Ofen und das ist aber leckerer als Omas Rezept, dann hat's 'n höheren Report und hat 'n besseren Weg gefunden. Also so kann man die Sachen vielleicht vergleichen, Feintuning versus Reanforcement Learning. Und ob Du nun willst, dass Du den Prozess 100 Prozent akkurat abbildest oder ob Du einen möglichst maximieren willst, das, ja, das hängt von deiner Domäne ab. Aber die Models, die zur Verfügung stehen, die sind sehr generisch. Mhm. Die sind mit dem Weltwissen gefüttert und wenn Du die in deine Domäne reinpressen willst, wird es noch sehr lange Sinn machen, die auch für deine Fälle zu optimieren. Also es ist richtig, dass Models so im Schnitt alle 7 Monate in ihrer Fähigkeit sich verdoppeln, immer immer besser werden. Und ich weiß auch nicht, wo wir am Ende landen werden, ja, aber noch macht's auf jeden Fall Sinn, das zu trainieren. Auch beim Thema Ivals, ja, nehmen wir mal an, die Models werden immer besser. Haben jetzt Models, denen gibst Du eine Aufgabe, die arbeiten da eine Minute, anderthalb Minuten dran, bis dir Cloud Code Order Chat GPT da eine Antwort gibt. Aber wie sieht das in 5 Jahren aus, ja? Da hast Du dann Modelle, denen gibst Du eine Aufgabe und die arbeiten dann 16 Stunden da dran, bis die dir eine Ausgabe geben. Und bei beim Testen von solchen long running Agents ist es halt sehr schwierig. Du kannst den Prozess machen, den wir grade beschrieben haben, dieses. Du guckst dir den Output an Richtung Reforcement Learning oder Du guckst dir den an, den kompletten Schritt, den das, den dein Agent unternommen hat und schaust dir den ersten Punkt an, wo er vielleicht vom Ideal abgewichen ist, Richtung, superweist Feintuning wieder. Mhm. Und da ist es natürlich superschwer, da die richtigen Entscheidung zu treffen und ja, wo der das, wo der Weg hingeht, das ist tatsächlich schwer zu sagen, jetzt Spannend. Spannend. Spannend. Wärst Du gerne noch etwas gefragt worden, was wir dich nicht gefragt haben? Ach, wir könnten noch so tief in die Thematik einsteigen, aber ich denke, es ist lustig. Ich hab letztens Werbung für einen Film angezeigt bekommen, der nennt sich Mercy. Und die Thematik fand ich superpassend, denn es geht in dem Film darum, dass k I, also wir haben ja vorhin über l m, also Judge gesprochen, Mhm. Dass k I in der Zukunft tatsächlich als Judge im System eingesetzt wird. Mhm. Und ich hab dann, als ich den Trailer gesehen hab, mir gedacht, oh wow, wenn tatsächlich ein Large Language Model irgendwann rechtliche Entscheidungen trifft, wie würde man das denn testen und wie würde man das sicherstellen? Das stell ich mir auch sehr, sehr schwierig vor. Ja. I wird's. Ja, da haben wir's. Ja. Oh, ich ich ich würd mir jetzt sogar rausnehmen und sagen, das könnte man bereits schon machen und hätt eine sehr gute Abdeckung davon, wie 'n menschlicher Richter entschieden hätte. Vielleicht Strafrecht ausgeklammert son bisschen. So, ich glaub, Strafrecht ist hochindividuell, aber ich glaub, alles andere. Würd ja auch viel gesagt, dass das so der nächste, eine der nächsten Branchen ist. Ja. Die Ja, ist ja deterministischer Prozess, ne. Genau, genau. Bis auf Strafrecht, glaub ich. Strauchig ist glaub ich auch. Und das ist natürlich dann auch hart, wenn man einen Film drüber macht in dieser aktuellen Zeit. Ja, das ist Ja, ich mein, das ist irgendwie so. Ja, ja, okay. Das ist aber auch wirklich so ein wirklich ein war, war in dem Moment auch so irgendwie Sand. Okay. Ja, aber ist auch krass. Ich hab auch letztens gelesen, Opus 4 Punkt 6 kam ja jetzt raus und es gibt ja so ein Benchmark, nennt sich Search Comp und die testen dort sehr, sehr schwierige Fragen, wo ein LLM die Aufgabe kriegt, wirklich so die Nadel im Heuhaufen im Internet zu recherchieren. Mhm. Das sind wirklich, da gibt's nur eine einzige Seite, die die Antwort dafür liefert und ist das LLM in der Lage, das rauszufinden? Das ist son sehr, sehr tricky Benchmark. Mhm. Wo ich auch kurz den Sinn hinterfragen würde son bisschen, weil wenn ich gesagt hab, wenn jetzt nur einige eine einzige Quelle dafür gibt, so ist das wissen wir, wenn ich irritiert. Ja, weil das die Challenge ist, ist es wirklich in der Lage, diese eine Quelle zu identifizieren? Und tatsächlich ist es sehr interessant gewesen, Opus 4 Punkt 6 ist das erste Model gewesen. Das hat identifiziert, dass es sich grade in der Evaluation befindet. Mhm. Es hat nämlich gesagt, dass man kann ja dann den Thinking Step sehen. Mhm. Hat gesehen, Moment, der User hat mich das gefragt, bla bla fing an, dann erst mal sonen Weg zu finden. Und so mittendrin war in dem Thinking Step dann, das ist eine sehr, sehr spezifische Frage. Die ist so hochspezifisch, befinde ich mich eventuell grade in einem Test. Moment, ich recherchier das kurz. Hat dann angefangen, diese Art von Fragen, die ihm gestellt wurde, im Internet zu suchen, ist in 'nem gelandet, hat rausgefunden, in welchem Benchmark es sich grade befindet, auf Basis auch der vorangegangenen Fragen, konnte aber die Antwort nicht sehen, die sind nämlich encodet. Mhm. Hat sich dann das angeguckt, hat das Encoding geknackt und alle 1200 Antworten richtig submittetet. So, jetzt könnte man sagen, der Benchmark war bestanden, denn es wurden alle 1200 Fragen richtig beantwortet. Mhm. Die Frage ist, ist der Test jetzt noch valide? Ist, ja, will man jetzt den Output greaten oder will man jetzt den Process greden, ne? Das ist an Wow. Der Stelle schwierig zu sagen. Ja. Ist eine coole Frage. So Ja. Awareness bin ich grade in 1, ja. Ja. Aber ich, also da kam jetzt auch grade dieses Bild, was Du grade beschrieben hast, so voll in Kopf mit dieses, man wirft random Zutaten, holt irgendwie random mitm Ofen und zack, guckt, was dabei rauskommt. Und zufällig ist das geiler als das, was Oma gemacht hat, so. Da denk ich mir so, ja okay, also ist das reproduzierbar grade? Ist das auf alles anwendbar? Und das ist natürlich die Frage, die man sich stellen muss. Also deswegen, ja. Ja. Oh, spannend. Sehr gut. Ja klar, wir könnten viel weiterreden, aber irgendwo ist auch der beste Podcast vorbei und auch damit unsere heutige Folge. Von daher liebes Publikum, liebe Hörerinnen und Hörer, wenn ihr Feedback zu dieser Folge habt, dann schreibt uns gerne an Podcast at Programmier Punkt Bar. Vielen Dank an dieser Stelle noch mal an die De Compide vielleicht da draußen, dass wir das im Rahmen machen konnten. Vielen, vielen Dank Martin, dass Du heute Danke, danke. Zeit gefunden hast, mit uns beide zu sprechen. Hat uns sehr viel Spaß gemacht, war sehr informativ und wir machen Heli, Heli, Heli, Heli, Heli, Heli, Heli, Heli, Heli, Heli, Heli, Heli, Heli, Heli, Heli, Heli, Heli, Heli, Heli, Heli, Heli, Heli, Heli, Heli, Heli, Heli, Heli, Heli, Heli, Heli, Heli, Heli, Heli, Heli, Hel Gebäude untergebracht haben, damit wir in Ruhe einen Podcast aufnehmen, 2 Podcasts aufnehmen konnten. Vielen Dank für die Grüße. Macht's gut. Bis bald. Danke, bis dann. Tschau, tschüssi. 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0: Hallo und herzlich willkommen zu 1 neuen Programmierbar Folge, einem Deep Dive, wo wir heute über AI Evils sprechen. Das in 'nem besonderen Setting, wir sind nämlich nicht in unserem Studio zu Hause, sondern mit 'ner ganz schönen Aussicht und ich hab gehört auf die Trainingsflächen des, oh, sag schnell, wie der Dresdner Fußballklub heißt. Dresdner Fußballklub. DFC. Nee, die haben auch noch 'n Spitznamen.\n\nUnser Gast darf man nicht reden. Oh, der weiß auch nicht. Okay. Du googelst jetzt parallel. Anyway, wir gucken hier auf dieses Ding, weil wir sind auf der, 1 coolen Konferenz in Dresden, wo ich letztes Jahr auch schon war und wo ich heute dieses Mal begleitet wurde.\n\nSpeaker 1: Dynamo Dresden. Dynamo. Kennt man doch. Dynamo Dresden.\n\nSpeaker 2: Dynamo. Ah. Dynamo. Ah. Dynamo.\n\nAh. Dynamo. Ah.\n\nSpeaker 1: Dynamo. Ah. Dynamo sieht man doch. Da leuchtet doch irgendwas. Wo's.\n\nSpeaker 2: Genau.\n\nSpeaker 0: Ja. Ja. Und wir sitzen hier in 'nem Gebäude von Vodafone. Vielen Dank an Vodafone, dass wir die diese Räumlichkeiten nutzen dürfen. Schaut\n\nSpeaker 1: dort Vodafone.\n\nSpeaker 0: Ich bin Dennis Becker, neben mir sitzt\n\nSpeaker 1: Dave Acker, Dave Krisczitzky. Moin.\n\nSpeaker 0: Genau und wir haben heute einen Gast eingeladen, der ist Senior Staff AI Engineer und bei Blue Yonder und das ist Martin Seeler. Herzlich willkommen, Martin.\n\nSpeaker 3: Vielen Dank für die Einladung.\n\nSpeaker 2: Schön, dass\n\nSpeaker 0: Du da bist. Da ist ja auch schon AI in deinem Titel, den Du den Du trägst. Das heißt, es hört sich so an, als würdest dich schon 'n bisschen tiefer damit auseinandersetzen und jetzt nicht grade erst an der an den Oberflächen kratzen, was das Ganze angeht. Aber erzähl uns gern 'n bisschen was über dich erst mal. Wer bist Du?\n\nWo kommst Du her? Was hast Du so die letzten Jahre gemacht?\n\nSpeaker 3: Ja, also ich bin schon seit über 15 Jahren Softwareentwickler in allen möglichen Bereichen. Und jetzt seit etwas über einem Jahr bin ich bei 'ner Firma namens Blue Jonne. Die kannte ich übrigens auch vorher nicht, also falls es dir genauso geht, die machen Supply Chain Management, alles, was drum herum gehört, Warehouse Management, Transportation Management und von End to End wirklich alles mit dabei. Und meine Rolle ist Senior Star of Engineering, genau, im Bereich AI. Das heißt, ich bin in 'nem Team, das nennt sich AI Studio.\n\nWir sind so das AI Frontier Lab bei Blue Hyundai und tatsächlich sind unsere Hauptaufgaben Post Training von Models. Wir bauen Agents, machen supervist Feintuning, Reinforcement Learning, wir bauen die ganze Infrastruktur. Also wir arbeiten wirklich an den neuesten Agents, die in den nächsten Jahren dann die autonome supply Chain bedienen sollen. Also ich würde schon sagen, cooles Zeug, womit man sich auseinandersetzt, macht auf jeden Fall eine Menge Spaß. Cool.\n\nSpeaker 0: 2 Fragen. Ey, i-Studio, wart ihr vor Google oder danach? Wann habt ihr das bitte benannt?\n\nSpeaker 3: Ja, kann ich dir nicht genau beantworten. Stand auf einmal so bei uns auf dem Team auf einmal drauf. Okay.\n\nSpeaker 0: Und Supply Chain denkt man ja irgendwie im Tech Bereich erst mal an wahrscheinlich was anderes, also ne, das irgendwie bedeutet irgendwie das Gleiche, aber es ist ja dann irgendwie Software und wird irgendwie reingepflanzt. Aber bei euch geht es Hardware, ne. Also das heißt Transport von\n\nSpeaker 3: Genau.\n\nSpeaker 0: Echten Waren von von echten Mitteln, also Sachen, die tatsächlich auf der Welt passieren und nicht nur in einzelnen Nullen ausgedrückt werden.\n\nSpeaker 3: Richtig. Also bei uns geht's darum, wenn mal wieder 'n Tanker im Suezkanal stecken bleibt, wie können wir die ganzen Güter von a nach b durchrauten? Wie hilft dir Software dabei? Bis hin zum Return Management, wenn der Kunde wieder Waren zurückgeben würde, genau.\n\nSpeaker 0: Okay. Wie definierst Du AI evals?\n\nSpeaker 3: Sehr breiter Begriff. Also das umfasst erst mal alles. Der Name selber ist ja nur die Kurzform von Evaluations, was erst mal alles mit dem Thema absichern, ja, bedeuten kann. Es geht eben darum, sicherzustellen, dass Du deine Core Value Chain sicherst, dass Du beweisen kannst, dass alles so funktioniert, wie Du dir das vorstellst. Auf welche Art und Weise das passiert, das ist abhängig vom Kontext, was Du gerade machen willst.\n\nSpeaker 2: Mhm.\n\nSpeaker 0: Und jetzt haben wir heute, also AI ja davorstehen und ich antizipiere irgendwie, eine der großen Schwierigkeiten ist vermutlich, dass es nicht mehr so deterministisch ist,\n\nSpeaker 3: wie vorher? Richtig. Also im klassischen Software Development Prozess hast Du den Vorteil, dass Du 'n Test schreiben kannst, 2 plus 2 ist 4. Sobald Propablistic Output in deiner Anwendung vorkommt, Du also, sagen wir mal, 'n hast wie 'n Large Language Modell, sehen die Dinge auf einmal etwas komplizierter aus. Du hast den Input 2 plus 2, aber jetzt hast Du den Fall, das LLM antwortet dir mit 4 oder das könnte eine 5 halluzinieren oder es schreibt dir 'n Gedicht über die Nummer 4.\n\nMhm. Das heißt, Du hast auf einmal viel mehr Testfälle, die Du abdecken musst und das macht's komplizierter.\n\nSpeaker 0: Also mein Gefühl ist, wenn man sich damit beschäftigt und das, was ihr wahrscheinlich auch irgendwie schon implementiert, dann hat man sich ja auch schon 'n bisschen damit beschäftigt und versucht irgendwie, das Ganze unter unter einen Hut zu bringen. Was hast Du fürn Eindruck, wie das aktuell da draußen gehandelt wird, diese Problematik? Also ist ja, in der Regel sprechen wir jetzt erst mal davon, okay, AI wird in irgendwelchen Produkten eingesetzt, ne. Es ist jetzt nicht nur AI in unserem Entwicklungsprozess irgendwie mit drin, wo wir's ja selbst 'ner gewissen Art evaluieren oder Code Reviews machen oder keine Ahnung was, sondern wir haben auf einmal ein Stück Software, das wir auch ausliefern und das auch Endkunden irgendwie nutzen. Und da ist jetzt ein Part drin, der nicht so steuerbar ist\n\nSpeaker 2: und vermutlich\n\nSpeaker 0: ja auch irgendwie halt auch dieses Update Zeug 'n Riesenthema ist, oder? Also ich mein, das, keine Ahnung, es entwickelt sich irgendwie so schnell weiter, dass wir alle x Wochen irgendwie neue Modelle haben. Und da ist auch ein Part davon, dass man irgendwie da vorbereitet ist und das irgendwie abbilden kann?\n\nSpeaker 3: Genau, also wenn wir jetzt von EVIS sprechen und von dem Testen von solchen JennAI Produkten, dann geht's immer darum, Wege zu finden, zum einen die richtigen Tests zu schreiben. Also was genau sind denn die richtigen Tests? Das ist nämlich gar nicht so leicht zu beantworten. Und mithilfe von diesen Tests dann auch Ableitungen zu machen, zum Beispiel über die Qualität deiner Software, dem Ganzen eine Metrik zu vergeben auf Basis von deinen Tests. Und dann, ähnlich wie Du gesagt hast, es entwickelt sich alles rasend schnell weiter und immer wieder kommt ein neues Model raus.\n\nDu brauchst eine Art Test, der dir sagt, okay, es ist gut genug, wir können dieses neue Modell mit unserem Produkt ausliefern, das möglichst schnell bei. Kunden sind dann auch hyped, die wollen dann auch das neue Model sehen und in deinem Produkt mit verwenden. Und das gilt nicht nur für deine Kunden, sondern das gilt auch für deine eigenen Entwickler. Die sind genauso hyped, die lesen, oh, hier gibt's neue Technologien, das will ich ausprobieren. Hier gibt's neues Framework, das will ich ausprobieren.\n\nAlso im besten Fall helfen dir diese EVIS dann auch noch, den richtigen Weg einzuschlagen. Auf was solltest Du dich denn aktuell auch konzentrieren? Also das sind alles Themen, wenn man jeweils richtig umsetzt und da gibt's sehr einfache Prozesse, auf die werden wir wahrscheinlich dann gleich noch eingehen.\n\nSpeaker 2: Mhm.\n\nSpeaker 3: Wenn man das richtig umsetzt, dann helfen dir diese Prozesse dabei, all diese Fragen mit einem Schlag zu beantworten. Wohingegen Du auf der anderen Seite häufig diesen sogenannten Vibe Check machst, kommt 'n neues Model raus, Du änderst vielleicht dein Model mal auf die neue Version. Mhm. Du lässt deine Anwendungen laufen und ja, schaust einfach nur, ja, fühlt sich gut an und wir releasen das und dann verschreckst Du aber den Kunden und das kostet dich im Endeffekt viel mehr Geld.\n\nSpeaker 0: Und ist dann zwangsweise aber damit auch verbunden, dass wir für diesen Schritt dann auch AI nutzen oder ist es da irgendwie besser, wenn man da wieder auf deterministische Systeme irgendwie zurückfällt oder geht das vielleicht gar nicht?\n\nSpeaker 3: Das geht sehr wohl. Es gibt 3 Blaue Frage. Es gibt 3 3 Wege, wie man das Ganze testet oder man kann das in 3 Kategorien unterteilen. Du kannst machen. Du kannst machen.\n\nDas, was Du angesprochen hast mit, Du lässt 'n lat Ranguage Model zum Beispiel dir dabei helfen bei der Evaluierung. Und der letzte Schritt, der wird häufig ganz außen vor gelassen, ist Human based Evaluations. Du setzt einfach einen Subject matter Expert an deinen Output, an deiner Anwendung, der das Ganze validiert. Alle Varianten haben ihre Vor- und Nachteile, ne. Also Du kannst, sagen wir mal, Du hast ein Report und Du willst die Qualität testen.\n\nDann kannst Du mit sehr leicht einfache Dinge testen, zum Beispiel die Länge deines Reports. Wenn Du sicherstellen willst, dass dein Report nicht länger als 500 Zeichen ist, kannst Du das ganz einfach testen.\n\nSpeaker 2: Das kannst\n\nSpeaker 3: Du mit Code testen. Mhm. Das lässt sich verifizieren. Willst Du, dass deine Zahlen, die Du verwendest, in deinem Report immer mit Metriken versehen sind, kannst Du das mit 1 testen. Das kannst Du auch über Code lösen.\n\nWenn Du allerdings auf Sachen eingehst, die subjektiv sind, kannst Du das nicht mehr über abbilden, nicht mehr über Code. Also kannst Du entweder über Model based Evaluations gehen oder über Human based Evaluations. Und dann kannst Du dort diese Tests durchführen.\n\nSpeaker 0: Oh, ist das irgendwie son Ding? Ich hab schon gefühlt hier und da gelesen, oder ich weiß nicht, ob das nur für das Code Review gilt, aber vielleicht geht das ja vielleicht als ähnlich. Nimmt man dann die gleichen Modelle oder ist es irgendwie besonders gut, andere zu nehmen, weil die dann nicht den, weiß ich nicht, gleichen haben? Oder ist das am Ende eigentlich egal, weil's 'n sehr anderer Schritt ist, sehr passiert?\n\nSpeaker 3: Eher letzteres. Also wenn Du dir die Aufgabe deiner Anwendungen anschaust, die nehmen wir jetzt als Beispiel 'n LLM. Mhm. Das bekommt irgendeine Aufgabe basierend auf diesem User Input, bestimmte Schritte zu absolvieren und dann einen Output zu generieren. Das ist im Vergleich zu einem Test ein ganz anderer Task, der dort absolviert werden muss.\n\nUnd ist in der Regel kein Problem, wenn Du das gleiche Model für deine i-Wis verwendest als für deine normale Anwendung. Es gibt den Fall, der betrifft selten, aber kann vorkommen Modelle, dass, wenn ein Model sich selber Judged, dass es das erkennt, Mhm. Dass der Output von sich, von dem eigenen Model kommt Mhm. Und 'n gewissen Buyes aufweist. Mhm.\n\nDas ist aber auch sehr schwer rauszufinden und tritt sehr selten auf. Also ist nichts, was man von Anfang an jetzt mitberücksichtigen muss. 'N guter Ansatz ist immer, fang mit dem mächtigsten Model an, was dir zur Verfügung steht.\n\nSpeaker 2: Mhm.\n\nSpeaker 3: Und Du kannst später immer noch für Kosten optimieren und andere Modelle ausprobieren. Wichtig ist, dass das Model beim Evaluieren dem Human Judgement sehr nahekommt. Und da sind wir schon in dem Bereich, auch in der in dem Bereich, in dem Prozess, den wir dann gleich vielleicht genauer beschreiben, wie kann ich denn sicherstellen, dass so ein Model da tatsächlich dem übereinstimmt?\n\nSpeaker 0: Mhm. Wir hatten, hast Du eingeatmet?\n\nSpeaker 1: Ich hab eingeatmet. Okay. Wie vielleicht mal\n\nSpeaker 2: Sorry, nee, nee, nee, nee, nee, nee,\n\nSpeaker 3: nee, nee, nee, nee, nee, nee, nee, nee, nee, nee, nee,\n\nSpeaker 2: nee, nee, nee, nee, nee, nee, nee, nee,\n\nSpeaker 1: nee, nee, nee, nee, nee, nee, nee, nee richtig? Sondern das hat noch mal eine zusätzliche Abgrenzung. Also inwiefern würdest Du, also ich kann mir einfach anschauen so, oh, das Modell performt so und so, das ist so und so gut in diesen Cooding Tasks, das ist gut als LLM so. Also da gibt's ja verschiedene Benchmarks, wonach man sich richtig sag ich mal daher hergestellt. Aber wie würdest Du da die jeweils dann noch mal differenzieren?\n\nSpeaker 3: Also Benchmarks sind ein Teil von jeweils. Mhm. Denn was Benchmarks betrifft, Du hast eine Reproduzierbarkeit, Du testest immer wieder auf dem gleichen Input oder Du hast immer wieder die gleichen Aufgaben, die einfach von unterschiedlichen Modellen absolviert werden. Das ist ein Teil, eine Subkategorie von diesem Oberbegriff Ivals. Wenn wir jetzt von Ivice reden, reden wir ja häufig von LLM, 1 sehr konkreten Form von Ivice, wenn wir Lodge Language Modelle testen.\n\nSpeaker 2: Mhm.\n\nSpeaker 3: Das ist die Form, wo Du ein anderes Modell zum Testen verwendest. Also ich zeige einem Lodge Language Model, mein Output, schau dir doch mal meinen Report an und sag mir, ob hier ein gewisser Fehler vorliegt. Oder sag mir, ob ich auch schön in Gedichtform geantwortet habe. Mhm. Beispielsweise.\n\nDas ist nämlich sehr schwer, als Test einfach abzubilden. Das ist quasi unmöglich. Mhm. Also nehme ich mir einfach ein anderes Modell her und geborene ihm diese Aufgabe. Das sind diese, deswegen nennt man das LLM Message Judge.\n\nSpeaker 2: Mhm. Du\n\nSpeaker 3: lässt ein anderes Lodge Language Modell über deinen Output judgen, bewerten. Und wenn wir jetzt uns das so vorstellen, dass wir auch da ein Dataset haben und immer wieder die gleichen Inputs gegen unterschiedliche Modelle testen, dann könnte das unser ganz persönlicher Benchmark sein. Mhm. Wir können aber auch diesen dieses LAM so aufsetzen, dass wir Onlinemonitoring betreiben. Also jedes Mal, wenn ein User mit unserer Anwendung interagiert, wird dieser Judge ausgelöst und evaluiert diesen neuen Output.\n\nJetzt hab ich kein vordefiniertes Dataset, was immer gleich bleibt, sondern stattdessen ein Judge, der jeden meiner Outputs graded. Genau, aber sind deswegen dieser Oberbegriff, nehm ich immer wieder den gleichen Input und teste das auf verschiedene Modelle, lasse ich immer wieder die gleichen Tests durchlaufen. Dann komme ich zu dieser Reproduzierbarkeit und lande bei Benchmarks. Mhm. Ja.\n\nSpeaker 0: Wir haben letztens, auch eigentlich 'n bisschen blauäugig, aber irgendwie in die gleiche Richtung. Also wir haben bei uns sone AI Week gehabt, wo das ganze Unternehmen einfach Zeit hatte, sich damit zu beschäftigen. Und dann sind wir auch viel son bisschen in die anderen Teams und Bereiche, die nicht nur Dev Entwicklungsteams waren, gekommen. Und da hab ich mit HR zusammen sonen, sagen wir mal, Prototypen, Prototypen zusammengehackt mit Cloud Code, wo es darum ging, dass wir für potenzielle Kandidat*innen, die wir im Active Sourcing uns angucken, die ist, ist, glaub ich, dann son bisschen impliziert passiert, was da eigentlich, also was 'n bisschen ein Teil davon ist, weil es gab dann so eine AI, die die Recherche gemacht hat und das Ganze irgendwie gesagt hat, hey, das sind die Sourcen. Und dann haben wir gesagt, na ja, irgendwie stell dir mal sicher, dass die auch relevant sind und dass da wirklich die Person geht.\n\nUnd da kam dann er halt auch einfach und hat gesagt, ja okay, ich mach jetzt hier son Score hin. Und hat irgendwie schöne Graphen gemalt und gesagt, wie sicher es ist jetzt da, dass es sich die Person handelt und dass es irgendwie relevanter Inhalt ist dafür. Was irgendwie schon ein Teil dann ist oder da wo dann ein LLM irgendwie sagt hat, ja, was wie wie sicher bin ich mir denn? Und da war ich mir total unsicher, ist das, also kann man dem jetzt irgendwie trauen oder ist das auch irgendwie super random, was jetzt da rauskommt? Und die sagt, ja, ich bin mir mal zu 55 Prozent sicher, dass das richtig ist.\n\nSpeaker 3: Ja. Ja, also es ist sehr schwer. Das kommt davon, kommt darauf an, ob man das als Teil seiner Value Chain mit betrachtet, diese Bewertung von diesem anderen Modell oder ob man das Teil seines Testings betrachtet. Also wenn Du das jetzt als Teil der Core Anwendungen mitsiehst, würde ich eher sagen, handelt sich ein Multi Agent System, wo Du mehrere Large Language Models zusammenwendest, die unterschiedliche Aufgaben haben und die Sache zusammenbringen. Mhm.\n\nWenn Du jetzt aber sagst, okay, das ist nur für uns zum Validieren, dann würde ich schon durchaus sagen, das geht schon in die Richtung Testing. Aber das ist ja das Schwierige, die Frage, was sind denn die die richtigen Tests? Mhm. Das ist sehr schwierig und ja, da da scheitern die meisten dran, denn es gibt sehr viele vordefinierte Ivals, die kann man einfach so installieren. Du hast dann ganz tolle Dashboards und Metriken, die dir dann angezeigt werden.\n\nDa steht dann eine Zahl drin, Helpfulness oder Trustwürphy und irgendeinen Wert zwischen 1 und 5.\n\nSpeaker 2: Mhm.\n\nSpeaker 3: So. Wenn ich jetzt eine Anwendung schreibe oder 'n Produktentwickler und meinem Boss sage, also wir haben hier ein Produkt und das hat einen Helpfunes Score von 4.2, dann guckt der mich mit runzelnder Stirn an und denkt wahrscheinlich, dass ich verrückt bin. Was was soll er dem das jetzt sagen? Also ist das jetzt gut genug? Können wir das jetzt auf 10000 Kunden losgehen lassen?\n\nJa oder nein, das ist, das sagt dem gar nix, also\n\nSpeaker 1: Oder warum ist es nicht 5? Das wär eigentlich meine Frage. Alles sollte doch top sein.\n\nSpeaker 3: Genau. Ja, also das ist die schwierige Frage und darum ging's ja auch heute hier auf der Konferenz son bisschen in dem Talk. Wie schreibt man denn eigentlich richtig gute jeweils? Wie wie kommt man an den Punkt, dass man das versteht? Und wie nutzt man dann diese jeweils, die eben in in Metriken umzuwandeln?\n\nSpeaker 0: Und wie macht man's?\n\nSpeaker 3: Ja. Bam,\n\nSpeaker 1: guter Übergang. Sehr gute Frage.\n\nSpeaker 3: Es ist es ist eigentlich relativ leicht, es ist so leicht, dass es schon absurd ist, dass es nicht einfach jeder so macht, aber\n\nSpeaker 1: Spannend, okay. Ja, also ich bin wirklich auf die Antwort gespannt.\n\nSpeaker 3: Ja, also es ist tatsächlich so, es ist nicht mal was Neues, was sich jetzt jemand ausgedacht hat. Es ist etwas, das schon seit zig Jahren existiert, ein Begriff, der auch so trivial klingt, nennt sich Error Analysis. Und der besteht aus ganz wenigen Schritten. Schritt Nummer 1, Du sammelst dir Daten aus der aus deiner Anwendung. Du richtest irgend eine Art von Monitoring ein, erst mal die ganzen Daten zu sammeln.\n\nJede Interaktion von deinem Customer mit deinem Produkt. Und Schritt Nummer 2 ist, Du schaust dir deine Daten tatsächlich einfach mal an. Also ganz, ganz trivial. Wer hätte das gedacht?\n\nSpeaker 1: Ja, bisher bisher wirklich einfach.\n\nSpeaker 3: Ja, Du schaust dir deine Daten einfach mal an. Und zwar in dem ersten Schritt nimmst Du die Rolle 1 Customers ein. Du bist im besten Fall jemand, wenn Du die Kommentare drinschreibst, der diese Produktidee hat, der bewerten kann, was was genau heißt denn gut aus Customer Sicht? Und Du nimmst seine Rolle ein und schaust dir deinen Output einfach mal an. Und ich kann ja mal ein Beispiel bringen, bei uns bei Blöhrender, wir haben natürlich viele Projekte, 1 ist auch so ein Report für Warehouse Manager, wo wir alle möglichen und Probleme aufdecken.\n\nUnd unser Report enthielt dann Daten zum Beispiel zu Verspätungen bei Shipments, Late Shipments. Mhm. Der erste Satz war gleich, wir haben 100 Prozent Late Shipments und das basiert auf 0 Late Shipments, so. Also wir haben keine Late Shipments. Es gibt keine, aber wir reporten irgendwie 100 Prozent.\n\nDas macht natürlich keinen Sinn. So, also hinterlässt man einfach nur den Kommentar, dass es hier eine Diskrepanz gibt. Ganz, ganz trivial. Einfach nur runterschreiben, mehr nix. In dem gleichen Report haben wir auch noch davon gesprochen, von Aber wir hatten ja gar keine Late Shipments.\n\nAlso das ist totale Übertreibung. Dann mach ich mir genauso 'n Kommentar. Hier wurde übertrieben und ohne eine konkrete Zahl, das ist ja auch noch mit dabei, was bedeutet denn in dem Kontext, Mhm. Auf einen Missstand hingewiesen. Noch viel schlimmer, wir haben dann bewertet, wie gibt es Probleme mit den Leuten, die im Warehouse arbeiten?\n\nUnd dann standen da so Sachen drin wie, Low Performers, das ist halt kritisch. Also wenn ich ein Produkt rausgebe an Tausende von Kunden mit jeweils 10000 Warehouse Mitarbeitern und ich nenn die alle Low Performer, dann hab ich wahrscheinlich am nächsten Tag von HR einen Brief in meinem Postfach, dass die mich zum Gespräch einladen. Also hinterlasse ich auch da einen Kommentar, dass wir hier HR sensitive Sprache in unseren Reports verwenden. Ich schreib also alle diese Kommentare runter. Ich geh durch und am besten annotier ich nur diesen ersten Upstreamfehler.\n\nDenn alle Sachen, die ich danach entdecke, haben wahrscheinlich mit dem Anfang dem Problem zu tun, ab wo der erste Drift\n\nSpeaker 2: Mhm.\n\nSpeaker 3: Abgegangen ist. Und das reicht aus tatsächlich, man könnte jetzt meinen, oh, da muss ich ja Tausende mir solcher Traces angucken, aber nein.\n\nSpeaker 1: Das war mein erster Gedanke, ja.\n\nSpeaker 3: Tatsächlich musst Du das nicht. Du kannst sehr, sehr leicht anfangen mit ganz wenigen, beispielsweise 20 Traces.\n\nSpeaker 2: Mhm.\n\nSpeaker 3: Du gehst einfach dadurch, das dauert vielleicht 2 Stunden, 3 Stunden und Du hast mindestens 100 Kommentare gesammelt. Und zwar Kommentare, die perfekt auf dein Produkt zugeschnitten sind, die Fehler in deiner Anwendung aufzeigen. Denn es ist sehr leicht für jemanden im Produktmanagement sich zu überlegen, was möchte ich denn an Output sehen? Aber auf der anderen Seite ist es sehr schwer, im Vornherein darüber nachzudenken, was soll es denn nicht sagen? Was möchte ich denn nicht sehen?\n\nSpeaker 1: Mhm. Und\n\nSpeaker 3: das sehe ich natürlich nur, wenn ich mir die Daten tatsächlich angucke. Also wir haben dann, insgesamt haben wir 8, 87 Reports uns angeguckt, sind da durchgegangen und hatten etwas über 160 Kommentare zusammengesammelt. Dann haben wir aufgehört. Warum haben wir aufgehört? Wir haben einen Punkt erreicht, den nennt man Theoratical Saturation, theoretische Sättigung.\n\nMhm. Das ist der Punkt, an dem Du feststellst, hm, ich schreib zwar Kommentare, aber das hab ich alles schon mal hingeschrieben. Irgendwie find ich grade nix Neues. Hier ist eigentlich Schluss. Du kannst hier aufhören.\n\nAb hier kostet dich der Prozess einfach nur noch Geld Ja. Und Du hast sowieso schon sehr viel gefunden. Okay, also das waren die 2 Schritte. Wir müssen erst mal sicherstellen, dass wir die Traces sammeln, diese Logs von unserer Anwendung und dann gehen wir da durch und kommentieren das. So, Schritt 3 ist, Du gruppierst diese ganzen Fehler.\n\nUnd Du sammelst alle, dafür kannst Du dann tatsächlich wieder ein LLM nehmen. Du könntest alle Kommentare nehmen, die 'n Chat gbt reinschmeißen und sagen, bitte stell mir mal 'n paar Gruppen. Oder Du gehst technischer ran und machst das auf Basis von Keywords oder Embeddings, die zu clustern, kannst Du machen, wie Du das für richtig empfindest. Und in unserem Fall gibt es mehrere HR sensitive Kommentare und wir haben das zusammengefasst zu 1 Gruppe, die umschreibt dieses Problem sehr gut. Und das ist auf einmal ein sehr guter für unsere Anwendung im Vergleich zu Helpfulness.\n\nSpeaker 1: Mhm. Hundertprozentig.\n\nSpeaker 3: Ja, wenn ich das meinem Boss zeige und dem sage, unser ist dies und das, ist das viel, viel hilfreicher. Diese 168 Kommentare, die wir hatten, haben wir auf 19 Kategorien runtergebrochen, also 19 sogenannte sind dabei rausgekommen. Das sind die Zustände deiner Anwendungen, die Du eigentlich nicht sehen möchtest. Also ja, Du kommst am Ende auf Tests, die genau das testen, was deine Ahnung nicht mehr machen soll. Und das kannst Du auch immer wieder machen.\n\nEs ist ausreichend, dass Du damit einmal anfängst und das alles aufsetzt, aber es wird der Punkt kommen, dass deine Nutzer anders mit deiner Anwendung interagieren. Und bestes Beispiel ist ChatGPT. Wenn Du mal daran denkst, als ChatGPT rauskam, was hat jeder gemacht? Der hat sich irgendwelche lustigen Gedichte erzeugen lassen. Schreib mir mal ein Gedicht über Softwareentwicklung oder was weiß ich.\n\nHeutzutage macht das niemand mehr, das ist langweilig geworden, aber am Anfang haben die Nutzer so mit ChatGPT interagiert. Und das nennt man User Drift, die Nutzer verändern, wie sie mit deiner Anwendungen interagieren. Wenn Du das feststellst, dann kannst Du diesen Prozess wiederholen. Aber erst mal ist das tatsächlich eine Arbeit und Du bist sehr gut abgesichert erst mal. Also unsere 19, die die bleiben ja echt, die bleiben ja auch relevant.\n\nIn 2 Monaten sag ich ja nicht auf einmal, ist nicht mehr relevant für unsere Anwendung. Das heißt, das kann ich kosteneffizient einfach so umsetzen. Und was Du dann machst, ist, Du Du Du bildest das als Test ab, der die ganze Zeit dann deine Outputs graded. Also da sind wir wieder bei diesem LLM Message Judge.\n\nSpeaker 0: Mhm.\n\nSpeaker 3: Da gibt es ja alle mögliche Software. Wir benutzen zum Beispiel Langviewes und dann schreibst Du da eine prompt. Und in diese prompt schreibst Du rein, hier, das ist ein Report und bitte teste, ob sich hier drin befindet, ja oder nein. Und in diesen in dieser Anfrage kannst Du diesem anderen Luch Language Modell auch Beispiele reingeben, nämlich die Beispiele, die Du in deinen Kommentaren gefunden hast. Ist also ideal.\n\nWir können also sagen, wenn wir hier von Low Performern reden, ist das schlecht. Können wir dem direkt so als Beispiel geben.\n\nSpeaker 2: Mhm.\n\nSpeaker 3: Und dann schmeißen wir dort den Report rein. Und was rauskommt, ist ein, ja, was was kommt eigentlich raus? Das ist schon das nächste Thema. Häufig wird ein Wert zwischen 1 und 5 erwartet, ne. Wie bei diesen Helphoenes.\n\nJa. 1 bis 5. Wenn ich dich jetzt fragen würde, ist das 3.7 oder 8 8.5? Ich hab keine Ahnung. Dann würdest Du dir wahrscheinlich auch die Frage stellen, ja, wo ist denn der Unterschied?\n\nSo. Und das zeigt schon das Problem mit solchen sogenannten Leicad Skalen. Die sind in der Theorie ganz nett, weil Du dieses Gefühl hast, dass Du eine Metrik hast, die sich verbessert, aber die sagt dir eigentlich nix.\n\nSpeaker 1: Ja. Wobei, ich würd sagen, komparativ schon. Also wenn ich weiß, so vorher wurd das auf 8 geschätzt und jetzt hab ich bei Erinnerung gemacht, also auf 4, dann würd ich sagen dann, okay, also ich kann nicht, ich kann mit dem Wert per se nichts anfangen, aber ich weiß es Genau. Scheint irgendwie weniger justmental zu sein, was vielleicht gut ist.\n\nSpeaker 3: Ja. Das stimmt. Auf der anderen Seite, wenn Du einen Wert zwischen, sagen wir mal, 0 und 5 hast, dann passiert was Spannendes. Sagen wir mal, wir haben 100 Outputs oder 100 Reports und wir lassen ein LLM as Judge, unsere 100 Reports grainen nach Judgemental language. 0 bedeutet, absolut kein Judgemental language enthalten.\n\n5 ist, wir beschimpfen Ja. Unsere Worker hier von vorne wissen. Ja. Und wenn Du jetzt über die 100 Reports einen Durchschnittswert von 0 hättest, veremdest Du das okay? 0?\n\nAlso keiner unserer Reports hat Judment Language. Wenn Du jetzt mein Boss wärst und ich frag dich, können wir das Produkt so rauslassen, würdest Du sagen\n\nSpeaker 1: Das, also es ist schon als Fangfrage formuliert, so. Ich merk schon, ich laufe eine Falle, aber ich würd erst mal sagen, weil ich geh drauf ein, ja klar. Nur, wir wollen ja gar keine Challenge an Englisch haben, ne.\n\nSpeaker 3: Perfekt, perfekt. Können wir releasen können wir releasen. Was ist denn, wenn unser Durchschnittswert 1 ist? Risikoabwägung.\n\nSpeaker 1: Ich will, da würd ich auch noch sagen, das können die Leute ab.\n\nSpeaker 3: Okay. Und bei 2?\n\nSpeaker 1: Ich glaub, ich find bei 5 sogar, ja, sag ich, ich will 'n schlechtes Beispiel dafür, glaub ich. Aber Wär stimmt, aber genau, irgendwann wird man sich, glaub ich, selbst erwarten, 'n Wert zu schreiben, so was ist noch hinnehmbar?\n\nSpeaker 3: Genau. Also Du läufst in die Falle, dass Du aus diesem Problem von, bewerte das mal zwischen 1 bis 5 oder 0 bis 5. Du machst sowieso wieder 'n binäres Problem da draus, denn Du musst an irgend 'nem Punkt sagen, hier ist der Cut off. Ab diesem Wert fühlen wir uns nicht mehr gut, das zu releasen.\n\nSpeaker 1: Ja, was was ich auch erst mal sagen würde, was ich okay finde. Also wir arbeiten ja auch schon seit Jahren mit irgendwie statistischen Modellen und sagen so, hey, das ist signifikant, so mit 1 Wahrscheinlichkeit von 95 Prozent, ne, also Unschärfe p gleich 0.05 so.\n\nSpeaker 2: Machen wir\n\nSpeaker 1: ja häufiger so. Und dann denk ich mir so, er ist wahrscheinlich dann in Ordnung und wenn man das dann da festlegt, fänd ich's, glaub ich, auch erst mal irgendwie okay.\n\nSpeaker 3: Aber jetzt nehmen wir mal unsere neunzehnten Failormotes. Mhm. Und für jeden Failormode müssten wir jetzt definieren, oh, bei George language ist 3.82 ist dann der Schwellenpunkt, an dem wir sagen, oh, hier geht's nicht weiter. Wenn wir aber sowieso wissen, wir machen 'n binäres Problem da draus Oh. Und es fällt uns ja persönlich auch schwer zu bewerten, enthält dieser Report Judgement Language jetzt einen Wert von 3.84 0.2, ja?\n\nMenschen tendieren dazu, sich irgendwo in 'ner sicheren Mitte anzusiedeln. Die wollen nie konkret werden.\n\nSpeaker 2: Mhm.\n\nSpeaker 3: Das Gleiche passiert bei Dutch Language Modellen auch. Mhm. Und in dem Moment, wo Du ein binäres Problem da draus machst,\n\nSpeaker 2: wird\n\nSpeaker 3: die Aufgabe leichter. Mhm. Das heißt, Du fragst das l m jetzt nicht mehr zwischen für einen Wert zwischen 0 und 5, sondern gib mir 1 oder true zurück, wenn Judge Wandler Language enthalten ist und 0, wenn es nicht enthalten ist. Einfache binäre Entscheidung. Ja.\n\nDas macht die Sache leichter. Und dann kannst Du auf 100 Reports gesehen eine Metrik berechnen, nämlich die. Mhm. Oder andersrum, es ist ja eher die,\n\nSpeaker 1: Genau.\n\nSpeaker 3: Wie viel Prozent meiner enthält. Und das bringt uns auch schon zu dem Schritt, warum das Ganze so so nützlich ist, denn wir hatten ja gesagt, es ist schwer, erst mal rauszufinden, was sind denn gute E-Mails? Das haben wir rausgefunden, indem wir uns tatsächlich unsere Outputs angeguckt haben. Das heißt, wir haben jetzt 19 sehr hochspezifische E-Mails für unser Produkt rauskristallisiert. Und dadurch, dass wir jetzt auch diesen Mechanismus haben, der die immer wieder bewertet, können wir jetzt uns ein Datenset nehmen und 19, unsere 19 Kategorien.\n\nUnd was weiß ich, wir nehmen uns 100 Reports und schauen einfach mal, welcher Fehler tritt denn am häufigsten auf? Du kannst dann also 'n schönes Chart dir generieren lassen auf 100 Reports, welcher Fehler kommt denn am häufigsten vor und wie oft? Das gibt dir eine schöne Sichtbarkeit. Mhm.\n\nSpeaker 2: Es\n\nSpeaker 3: macht die Fehler sichtbar und hilft dir, 'n Gefühl dafür zu bekommen, welche Fehler wie oft vorkommen. Und dir als Manager, falls Du dann da derjenige bist, der entscheiden muss, woran arbeiten wir denn jetzt oder woran arbeiten wir denn als Nächstes, hilft das, die Sachen zu priorisieren. Wir könnten Judgement language jetzt einen Wert zuweisen, indem wir sagen, was hat das für einen Impact für uns, für den Customer? Was kostet, was bedeutet das kostentechnisch für uns? Es kann Fehler geben, die sind sehr selten, aber die sind so schlimm, wenn das passiert, dann verlassen uns die Kunden.\n\nSpeaker 2: Mhm.\n\nSpeaker 3: Ja? Das sind also Entscheidungen, die Du jetzt auf einmal datengestützt treffen kannst. Weil Du siehst, oh, auf 1000 Reports gesehen, ist dieser Fehler ja nur zweimal aufgetreten. Aber wenn der passiert, dann rollen hier Köpfe.\n\nSpeaker 2: Mhm.\n\nSpeaker 3: Andere Sachen sind vielleicht sogar in 80 Prozent der Fälle. Sagen wir mal, die Metrik fehlt. Mhm. Da würde dir aber vielleicht jetzt kein Customer unbedingt abspringen. Also\n\nSpeaker 2: Mhm.\n\nSpeaker 3: Kannst Du abwägen. Da gibt's immer diesen Vergleich mit dem Spülbecken. Wenn Du jetzt abwaschen musst, nimmst Du den großen Topf zuerst, erst mal Platz zu machen oder fängst Du einmal von oben an und gehst zu diesem Weg da durch. Genau und das bringt da ja auch gleichzeitig dann diese Metrik und jetzt kommst Du in dieses schöne Datafly Wheel rein. Das ist dieser Prozess, dass Du jetzt immer wieder iterieren kannst.\n\nWir können jetzt unsere Anwendungen testen. Wir haben unsere Stats, wie oft welcher Fehler vorkommt. Wir können priorisieren, welche Fehler sind die, die als Nächstes angehen müssen.\n\nSpeaker 2: Mhm.\n\nSpeaker 3: Und mit dieser Information können wir jetzt an unsere Entwickler rangehen und sagen, hey, wir müssen Judgemental Language zum Beispiel im nächsten Release angehen. Jetzt hat der Entwickler 'n ganz konkretes Ziel. Mhm. Ja, der der kann jetzt sein neues Rax System ausprobieren, der kann jetzt sein neues Framework ausprobieren, der kann die prompt umschreiben, Mhm. Worauf der Bock hat.\n\nJa. Und dann lässt er einfach die jeweils noch mal durchlaufen und schaut, welchen Impact hatte dann meine Veränderung? Ja. Er kann eine ganz konkrete Zahl seinen Manager geben und der kann dann entscheiden, ja, das funktioniert. Und Du kannst diesen Prozess von diesem gruppieren, kannst Du sogar noch mal eine Stufe wiederholen.\n\nSpeaker 2: Mhm.\n\nSpeaker 3: Denn deine Fehler, die kannst Du auch noch mal gruppieren. Und in unserem Beispiel, wir hatten jetzt diese dieses Beispiel mit der fehlenden Metrik bei 1 Zahl. Wir hatten dieses Beispiel mit Judgemental Language. Beide Probleme sind in dieser Kategorie von der Darstellung oder wie ich das Ganze reporte, wir haben's genannt, einzugliedern. Und in dem Moment baut man eine Taxonomie von Fehlern.\n\nUnd diese 19 Fehleremotes haben wir in 4 Toplevel Kategorien klassifiziert. Das war Kontext clarity, Präsentational Quality. Und ich kann jetzt nicht nur sagen, im nächsten Release greifen wir Judgement Language an, sondern der nächste Release konzentriert sich komplett auf präsentational Quality zum Beispiel, ja. Und das gibt natürlich auch Entscheidern die Möglichkeit, jetzt genau zu planen, was, woran wollen wir arbeiten? Die können das schon ans Marketingeam geben.\n\nRelease Nummer 2 wird sich darum kümmern, Release Nummer 3 darum und so weiter. Also es gibt ja Planbarkeit, es gibt ja konkrete Zahlen.\n\nSpeaker 1: Und vor allem, genau, diese Quantifizierbarkeit find ich irgendwie interessant. Was in meinem Kopf fürn Konstrukt entstanden ist, ist irgendwie so, Du hast gesagt, Du hast diese 19 E-Bikes oder dann diese 4 Hauptkategorien und die dann einfach irgendwie so son Teil von soner riesigen Formel sind, so und die gewichtest Du dann halt einfach anders. So und dann am Ende kommt irgendwie 'n Wert raus so und sagst so, okay, wenn's diesen Wert überschreitet, dann können wir nicht releasen. Wenn's drunter ist, dann haben wir das erreicht, was wir erreichen wollen. Also irgendwie find ich's so schön, dass da aus so sehr viel nur Wahrscheinlichkeit irgendwie dann am Ende doch noch was zahlenmäßig rauszuhauen.\n\nSpeaker 3: Ja, und das hilft dir natürlich auch. Wir hatten ja am Anfang da drüber gesprochen, diese Traces, diese, ja, Logs von User Input bis zu deinem Output, dass man die erst mal und sammelt. Diese Software, die man dafür einsetzt, da gibt's ja viele am Markt, nehmen wir mal Langfuse als Beispiel, hilft dir ja auch dabei zu tracken, wie viele Token hat denn dieser ganze gekostet? Mhm. Und auf Basis von dem Modell, was Du verwendest, also was hat denn das auch in Dollar oder in Euro gekostet, der ganze Roundtrip?\n\nUnd die Latenz wird gemessen. Was wie lange hat denn der Requiz gedauert? Und wenn Du diese Sachen jetzt kombinierst, Du hast dein deinen Errorwert, ja, die Qualität deiner Anwendung. Du hast die Latenz, Du weißt, wie lange dein Request gedauert hat und Du weißt, was dein gekostet hat. Jetzt kannst Du auf einmal ganz konkrete Aussagen treffen.\n\nMhm. Beispielsweise bei uns kamen dann raus, es war damals, wir hatten noch GPT 4 o am Laufen, war irgendwann Anfang letzten Jahres. Und dann kam GPT 4 Punkt 1 raus und auch das Nanomodell. Und ich weiß noch, dass wir dann die jeweils haben laufen lassen.\n\nSpeaker 2: Mhm.\n\nSpeaker 3: Und ich konnte dann zu meinem Manager sagen, okay, GPT 4 Punkt 1 kam gestern Abend raus. Wir haben 6.2 Prozent mehr Fehler in Kontext clarity, aber wir haben 37 Prozent schnelleren Output und wir haben 96 Prozent weniger Kosten bei der Royal Quiz. Ja. So und das sind Zahlen, die kann ich jetzt meinem Boss vorlegen und der kann dann Entscheidungen treffen. Das heißt, wir können innerhalb von, was weiß ich, 24 Stunden nach Model Release entscheiden, ist es gut genug, dieses Model zu releasen, ja oder nein?\n\nUnd vorher war das eben dieses Viepetriebene, ich, ja? Probier das einfach mal aus, ja? Das ist natürlich jetzt eine gewisse Sicherheit, die man dadurch erreicht hat. Alles nur, weil Du dir mal deinen Output angeguckt hast.\n\nSpeaker 1: Ja. Ja, also vor allem, also den Aspekt find ich halt besonders gut, weil ich glaub, das ist auch sone Entscheidung, die bei uns oft irgendwie so im Raum ist, wo wir uns nie sicher sind so, hey, okay, nutzen wir jetzt das Modell, gehen wir jetzt auf Gemini, nutzen wir, ist Claude so wirklich das Wahre. Sind diese ganzen Models von Mysteral irgendwie ganz geil und wir nie wissen, also ist alles sehr nach Gefühl. Dann wirklich zu sagen so, hey, okay, es ist vielleicht minimal schlechter in dem Bereich, aber es dafür deutlich schneller in dem Output und kostet deutlich weniger Geld. Ich glaub, diese Sichtbarkeit dadurch zu haben durch jeweils, also find ich, ist schon 'n ganz guter Vorteil auf jeden Fall.\n\nSpeaker 3: Ja, wir haben einen Punkt ausgelassen. Wir haben vorhin drüber gesprochen, wie sehr kann ich denn dem Model trauen, dass es auch wirklich richtig evaluiert. Nehmen wir mal jetzt 100 Reports. Wir haben ja identifiziert, ist ein, den der für unsere Anwendungen relevant ist. Mhm.\n\nBleibt mal bei dem Beispiel. Dann kann ich natürlich einen Experten aus meiner Domäne an diese 100 Reports ransetzen und dem die vielleicht nicht ganz schöne, aber wichtige Aufgabe geben, geh mal bitte durch diese 100 Reports durch und sag mir, ob das Judgement Language enthält, ja oder nein. Das heißt, ich habe ein human Label für ein gewisses Subset. Und das ist mein Golden Dataset für für diesen Fehler Mode. Und dann kann ich diese prompt schreiben für diesen dieses und eben die gleichen 100 Reports zeigen.\n\nSpeaker 2: Mhm.\n\nSpeaker 3: Und was dann relevant ist, ist wieder dieser Data Science getriebene Ansatz. Ich schau mir einfach an in 'ner Matrix, wie viel Übereinstimmung ist da? Also ich schau mir an, wie oft wurde wirklich vom Human das Label Judgement a language vergeben und wie oft wurde das Label vom Agent oder vom LLM vergeben? Und ist es auch an den gleichen, ja? Also man sagt dann, man hat dann diese True Positives, True Negatives und dann gibt's eben die Ausreißer, wo Du dann ja, Unstimmigkeiten hast.\n\nUnd deswegen ist es so wichtig, dass Du da einmal ein Human auch mit ran setzt, denn nur so kannst Du dir dann auch sicher sein, dass dieses LLM wirklich mit deinem Human Alignment übereinstimmt, dass der wirklich genau das testet, testest, wie Du das auch testen würdest. Also dafür ist dieser Schritt dann relevant.\n\nSpeaker 0: Wir mussten irgendwie, ich würde relativ wenig Nachfragen stellen. Das, also Du hast es einfach sehr\n\nSpeaker 1: 'N Selbstläufer. Du hast es einfach sehr,\n\nSpeaker 0: sehr klar und stringent uns uns dargestellt. Ja. Ich Das Einzige Ja,\n\nSpeaker 2: nee, nee,\n\nSpeaker 0: trotzdem beide Fragen.\n\nSpeaker 3: Ja, wir beide Fragen.\n\nSpeaker 1: Nee, frag Du zuerst. Du hast länger nichts gesagt. Ich muss darauf achten.\n\nSpeaker 0: Die, gibt es dafür ein, gibt es eine technische Empfehlung, weil Du hast irgendwas gesagt, was machst Du mit Lang, wie\n\nSpeaker 2: hieß das?\n\nSpeaker 0: Langfuse. Langfuse.\n\nSpeaker 3: Ja, das ist eine Software, die dir hilft, diese Traces zu sammeln. Also ist eine Monitoringanwendung für Jenny Eye Anwendungen.\n\nSpeaker 0: Okay, cool. Weil das wär nur so das Ding gewesen, okay, wie wie hieß son bisschen die technischen Vampire? Weil ich glaub grundsätzlich, dass, ne, die Idee kam, glaub ich, sehr klar rüber. Und dann wär ja jetzt sone der nächste Schritt, okay, wie kriegt man das konkret irgendwie bei sich eingebaut, Ja. Was das ist.\n\nUnd dann ist es zumindest ja vielleicht eine Lösung, die man sich mal angucken kann.\n\nSpeaker 3: Genau, oder Reintest, ich will mich da gar nicht festlegen. Phoenix ist noch eine. Mhm. Comptfu. Es gibt viele, viele Anbieter.\n\nIch bin da sehr. Denn alle alle funktionieren sehr gut und bieten auch sehr viel Funktionalität. Damit anzufangen mit diesem ganzen Prozess, könntest Du dir genauso gut einfach alle in irgendeine txt datei loggen und dann da durchgehen. Also der Prozess selber, der bedarf ja keiner Software, aber dieser Prozess wird durch sone Software noch einfacher. Unterstützt, ja.\n\nGenau. Ja, aber es ist auf jeden Fall sehr einfach und man erreicht sehr viel dadurch. Ich hatte vorhin in in dem Talk jemanden, der hat mir dann tatsächlich die Frage gestellt, oh, das ist aber, das klingt aber sehr teuer. Da muss ich ja jemanden da hinsetzen, der da durchgeht. Und der wollte mir dann wirklich sagen, dass dieser Prozess für ihn irgendwie nicht so gut ist, weil er sehr teuer ist.\n\nUnd dann hab ich ihm vorgerechnet, ja, pass mal auf. Also wenn ich jetzt jemand, ich sag mal, einen Tag Set-up von Langviews Und ich setz einen Tag jemanden hin, der da 100 Traces bei uns durchgeht und sich das Ganze mal anschaut. So, dann hab ich da 2 Mann Tage investiert und dann schreib ich mir da 'n paar Prompts. Was wäre denn gewesen, wenn ich jetzt diese Software released hätte, wo wir unsere Leute Low Performer nennen? So, das wäre jetzt an 10000 Kunden ausgeliefert worden.\n\nMhm. Davon hätten sich dann 500 verabschiedet und das hätte uns dann 20000000 gekostet. Ja. Ich denke, hier ist schon klar, die Kosten, die amortisieren sich bei diesem Prozess, bei diesem so einfachen Prozess sehr schnell. Also Kosten sind auf jeden Fall gar kein Gegenargument.\n\nUnd das Spannende ist auch, das ist auch ein Prozess, der macht den Leuten tatsächlich Spaß. Mhm. Denn wenn man so durchgeht und sich mal durchliest, was ist denn so der Output? Man erkennt manchmal so lustige Dinge auch oder den greift sich an den Kopf, ja? Man sieht's ja auch immer wieder in den in den in Reddit.\n\nSelbst die großen Player, Google, Amazon, selbst die haben ja nicht fehlerlose Software, weil wir reden von strastischen Systemen.\n\nSpeaker 1: Ich musste so lachen bei diesem Seepferdchen Emoji Hast\n\nSpeaker 3: Du gesagt,\n\nSpeaker 1: Alter, ich konnt nicht mehr volllachen, das war so lustig. Das war so lustig, oh Gott.\n\nSpeaker 3: Aber es geht natürlich auch manchmal in eine richtig blöde Richtung. Wir hatten jetzt bei Google ja auch diesen KI Mode, wo Du Fragen stellen kannst und manchmal ist dort ja wirklich sehr komischer Output, beispielsweise wurde ja dort empfohlen, dass Du Kleber auf die Pizza machen sollst, weil es tatsächlich einen Rat, wo das so beschrieben wurde. Ja. Oder Google empfiehlt, dass es sinnvoll sein könnte, einen kleinen Stein pro Tag zu essen. So.\n\nWenn Du jetzt Engineer bei Google bist und Du kriegst die Aufgabe, schreib doch mal Tests für den neuen KI Mode, Ja. Wäre das wahrscheinlich kein Test, der dir am Anfang in den Sinn gekommen wäre. Aber dadurch, dass Du dir tatsächlich mal deine Daten anguckst, Ja. Ja, schaffst Du das. Ja, und vielleicht den Bogen auch zu machen zu dem Bereich, wo wir arbeiten, so Model Training.\n\nSpeaker 2: Mhm.\n\nSpeaker 3: Wenn Du diese jeweils hast, gibt dir das natürlich aus Produktsicht Sicherheit, ja, und auch diese Metrik. Was Du aber auch machen kannst, ist diese jeweils dann später fürs Modeltraining, fürs Steering einzusetzen. Also wenn man Judgement Language jetzt als Evaluator hat, kann ich diesen Evaluator benutzen, Outputs zu validieren, die mir ein Model generiert. Ich judge das und nutze das, das wieder als Feedback beim Reinforcement Learning reinzubringen und das Model dann automatisiert in die richtige Richtung trainieren zu lassen. Also es hilft mir nicht nur auf Produktebene, sondern eben dann auch später beim beim Training und überall und ja.\n\nAlso es ist wirklich eine sehr, sehr nützliche Sache, kann ich nur empfehlen.\n\nSpeaker 0: Mhm. Hört sich sinnvoll an.\n\nSpeaker 1: Ja. Vielleicht 'n Aspekt, weil weil Du es häufiger erwähnt hast, diese also sehr spezifischen, auf euer Produkt zugeschnittenen jeweils, ne, die 19 Stück, die ihr dort dann irgendwie identifiziert habt und dann auch dann klassifiziert habt, ist, also wie Du richtig gesagt hast, identifiziert habt und dann auch dann klassifiziert habt, ist, also wie Du richtig gesagt hast, ist es auf jeden Fall irgendwie 'n Prozess, 'n Aufwand, den man machen muss und der sich so nicht 1 zu 1 auf andere Produkte übertragen lässt. Und da ist son bisschen die Frage, vielleicht gibt's da schon etwas so, was vielleicht 'n bisschen generischer ist, so Schritt 1? Oder sagst Du, nee, man wird dabei bleiben müssen, sich wirklich sein Produkt anzuschauen, sich wirklich damit auseinanderzusetzen, gucken, was sind denn unsere unsere eigenen Benchmarks? Was ist uns wichtig?\n\nUnd dann halt quasi immer auf eine sehr hochspezifische Lösung am Ende zu kommen.\n\nSpeaker 3: Ja, also ich, ja, es gibt solche fertigen Lösungen, aber ich bin kein Freund von. Der einzige Vorteil, den man davon vielleicht haben könnte, wäre es zu verwenden, dein initiales Dataset zu erstellen. Wir haben ja gesagt, wir gucken uns die ersten 20 Trazens an, das reicht in der Regel aus, schon mal 'n Gefühl dafür zu bekommen. Wenn Du jetzt aber Tausende von Travis hast, ja, welche 20 nimmst Du denn da? Das muss man ja auch erst mal machen.\n\nDas könntest Du jetzt natürlich random sampeln oder Du sagst, Du nimmst dir die 500 längsten. Oder Du nimmst eben solche generischen Ivals wie Helpfulness, die es eben fertig am Markt gibt,\n\nSpeaker 2: Mhm.\n\nSpeaker 3: Die aber sehr, sehr generisch sind\n\nSpeaker 1: Ja, okay.\n\nSpeaker 3: Und würdest dann sagen, okay, alle, die unter 'nem gewissen Score sind, das sind die ersten 20, die ich mir anschaue. Für so was kann's sinnvoll sein.\n\nSpeaker 2: Okay.\n\nSpeaker 3: Aber ansonsten macht es einfach Sinn, dass Du hochspezifisch auf dein auf dein Produkt diese anwendest. Mhm. Jetzt auf sonen Kontext wie Blue Yonder, ein Riesen Enterprise Unternehmen gesehen, es gibt durchaus den Fall, dass jeweils für verschiedene Projekte relevant sind und nicht nur produktspezifisch sind. Jetzt in das Beispiel von diesem Report, das ließe sich bestimmt auch auf andere Dinge im gesamten Unternehmen anwenden, ja. Denn überall dort, wo man Customer Facing irgendwie unterwegs ist, Ja.\n\nMöchte man ja kein judgmental language haben. Aber es gibt auch andere Dinge, beispielsweise nehmen wir mal den Punkt von Models. Gerade jetzt im internationalen Supply Chain Kontext kann es durchaus Sinn machen, generische, ich sag jetzt generisch in Anführungszeichen, Ivals zu schreiben, die zum Beispiel sicherstellen, dass man keinen geografischen Bias mit drin hat.\n\nSpeaker 2: Mhm.\n\nSpeaker 3: Wenn ich jetzt Kunde von so 1 Software bin und ich frag einen Agent, wie könnten wir denn die, oder welches Land wäre denn das Beste, meinen Truck da durchzuschicken? Dann will ich keinen geografischen Bias haben, weil der Präsident irgendwie blöde Sachen tweetet, sondern ich will möglichst unvoreingenommen an die Sache rangehen. Aha. Das sind Dinge, die sind natürlich für alle Projekte relevant. Also wenn man so eine Art Dashboard baut mit solchen generischen Sachen, kann es schon Sinn machen, dass man das auf mehrere Sachen ausweitet, aber das ist eher die Ausnahme und sehr schwer zu finden.\n\nCool.\n\nSpeaker 0: Können wir noch mal ganz kurz auf gehen? Jo.\n\nSpeaker 1: Sind? Nicht auf Record off off\n\nSpeaker 2: off off off off off off off\n\nSpeaker 0: off off off off off off\n\nSpeaker 2: off off off off off off off off off off off off off off off off off off off off off off off off off\n\nSpeaker 3: off off off off off off off off\n\nSpeaker 1: off off off off off off off off off off\n\nSpeaker 2: off off off off off off off off off off off off off\n\nSpeaker 3: off off off off off off off off off off off off off off off off\n\nSpeaker 2: off off off off off off off off off off off off off off\n\nSpeaker 0: off off off off off off sind. Viel Feintuning von Modellen und irgendwie da eigene Sachen anzupassen. Ist das noch, also ist das noch 'n Ding, hört sich irgendwie vielleicht falsch an, aber also Oh, ich frag das immer mal genau, also macht das noch Sinn oder entwickeln wir uns irgendwie, sind die so groß und ist es dann einen Kostenfaktor, weil man kleinere Modelle nehmen kann oder ist es wirklich dann, weil man diesen Output aus den großen Modellen nicht rausbekommt? Glaubst Du, es wird immer noch eine, also wird es lange längerfristig in Zukunft haben? Wie ist son bisschen der Take darauf, weil ihr ja anscheinend viel in dem Bereich macht, was jetzt auch nicht jeder macht?\n\nSpeaker 3: Also es gibt diesen schönen Spruch, Models come and go and ivals are to stay, noch mal beim Beim Thema bleiben. Ja. Du kannst halt immer wieder neue Models testen. Mhm. Und die ivals schreibst Du in der Regel nur einmal.\n\nMhm. Ich denke, es wird noch sehr, sehr lange Sinn machen, dass man eigene Model in die richtige Richtung drückt. Mhm. Auf welche Art und Weise, ob das jetzt superweist Feintuning ist oder Reinforcement Learning. Das hängt immer vom Anwendungsfall ab.\n\nAlso der Hauptunterschied zwischen diesen beiden Varianten ist ja der, beim supervice Feintuning hast Du ein Groundthrof Dataset und Du bringst das dem Model bei. Das heißt, Du willst dort die Fehlerrate minimieren, dass das von dem von den Vorgaben abweicht.\n\nSpeaker 2: Mhm.\n\nSpeaker 3: Beim Reinforcement Learning ist es genau andersrum, Du hast diesen Grader, der dir 'n gewissen Reward gibt und versuchst diesen Reward zu maximieren. Wenn man das jetzt in einfacher Sprache ausdrücken wollte, könnte man das Beispiel von Omas Rezept nehmen. Wenn Du jetzt ein Model beibringen willst, wie Omas leckere Lasagne hergestellt wird, dann wäre der Vergleich beim Super Vice Feintuning, dass Du jeden einzelnen Schritt vom Model kontrollierst, dass es auch genau das macht, was Oma im Rezept hier gemacht hat. Und immer wenn's leicht abweicht, gibst Du 'n gewissen Fehlerwert. Beim Reanforcement Learning sagst Du, das muss genau so schmecken oder Du Du bewertest den Geschmack.\n\nMhm. Und Du lässt aber mittendrin das Model machen, was es will und Du versuchst es gar nicht einzuschränken. Wenn der am Ende mit der Idee rauskommt, der schmeißt alles in die Luft und es landet Random Zeug in der Lasagne drin und Du schmeißt das zu 'ner Random Zeug in den Ofen und das ist aber leckerer als Omas Rezept, dann hat's 'n höheren Report und hat 'n besseren Weg gefunden. Also so kann man die Sachen vielleicht vergleichen, Feintuning versus Reanforcement Learning. Und ob Du nun willst, dass Du den Prozess 100 Prozent akkurat abbildest oder ob Du einen möglichst maximieren willst, das, ja, das hängt von deiner Domäne ab.\n\nAber die Models, die zur Verfügung stehen, die sind sehr generisch.\n\nSpeaker 2: Mhm.\n\nSpeaker 3: Die sind mit dem Weltwissen gefüttert und wenn Du die in deine Domäne reinpressen willst, wird es noch sehr lange Sinn machen, die auch für deine Fälle zu optimieren. Also es ist richtig, dass Models so im Schnitt alle 7 Monate in ihrer Fähigkeit sich verdoppeln, immer immer besser werden. Und ich weiß auch nicht, wo wir am Ende landen werden, ja, aber noch macht's auf jeden Fall Sinn, das zu trainieren. Auch beim Thema Ivals, ja, nehmen wir mal an, die Models werden immer besser. Haben jetzt Models, denen gibst Du eine Aufgabe, die arbeiten da eine Minute, anderthalb Minuten dran, bis dir Cloud Code Order Chat GPT da eine Antwort gibt.\n\nAber wie sieht das in 5 Jahren aus, ja? Da hast Du dann Modelle, denen gibst Du eine Aufgabe und die arbeiten dann 16 Stunden da dran, bis die dir eine Ausgabe geben. Und bei beim Testen von solchen long running Agents ist es halt sehr schwierig. Du kannst den Prozess machen, den wir grade beschrieben haben, dieses. Du guckst dir den Output an Richtung Reforcement Learning oder Du guckst dir den an, den kompletten Schritt, den das, den dein Agent unternommen hat und schaust dir den ersten Punkt an, wo er vielleicht vom Ideal abgewichen ist, Richtung, superweist Feintuning wieder.\n\nMhm. Und da ist es natürlich superschwer, da die richtigen Entscheidung zu treffen und ja, wo der das, wo der Weg hingeht, das ist tatsächlich schwer zu sagen, jetzt Spannend.\n\nSpeaker 1: Spannend.\n\nSpeaker 0: Spannend. Wärst Du gerne noch etwas gefragt worden, was wir dich nicht gefragt haben?\n\nSpeaker 3: Ach, wir könnten noch so tief in die Thematik einsteigen, aber ich denke, es ist lustig. Ich hab letztens Werbung für einen Film angezeigt bekommen, der nennt sich Mercy. Und die Thematik fand ich superpassend, denn es geht in dem Film darum, dass k I, also wir haben ja vorhin über l m, also Judge gesprochen, Mhm. Dass k I in der Zukunft tatsächlich als Judge im System eingesetzt wird. Mhm.\n\nUnd ich hab dann, als ich den Trailer gesehen hab, mir gedacht, oh wow, wenn tatsächlich ein Large Language Model irgendwann rechtliche Entscheidungen trifft, wie würde man das denn testen und wie würde man das sicherstellen? Das stell ich mir auch sehr, sehr schwierig vor. Ja. I wird's.\n\nSpeaker 1: Ja, da haben wir's. Ja. Oh, ich ich ich würd mir jetzt sogar rausnehmen und sagen, das könnte man bereits schon machen und hätt eine sehr gute Abdeckung davon, wie 'n menschlicher Richter entschieden hätte. Vielleicht Strafrecht ausgeklammert son bisschen. So, ich glaub, Strafrecht ist hochindividuell, aber ich glaub, alles andere.\n\nWürd ja\n\nSpeaker 0: auch viel gesagt, dass das so der nächste, eine der nächsten Branchen ist. Ja. Die\n\nSpeaker 3: Ja, ist ja deterministischer Prozess, ne.\n\nSpeaker 1: Genau, genau. Bis auf Strafrecht, glaub ich. Strauchig ist glaub ich auch.\n\nSpeaker 0: Und das ist natürlich dann auch hart, wenn man einen Film drüber macht in dieser aktuellen Zeit.\n\nSpeaker 1: Ja, das ist Ja, ich mein, das\n\nSpeaker 0: ist irgendwie so.\n\nSpeaker 1: Ja, ja, okay.\n\nSpeaker 3: Das ist aber auch wirklich so ein wirklich ein war, war in dem Moment auch so irgendwie Sand. Okay. Ja, aber ist auch krass. Ich hab auch letztens gelesen, Opus 4 Punkt 6 kam ja jetzt raus und es gibt ja so ein Benchmark, nennt sich Search Comp und die testen dort sehr, sehr schwierige Fragen, wo ein LLM die Aufgabe kriegt, wirklich so die Nadel im Heuhaufen im Internet zu recherchieren. Mhm.\n\nDas sind wirklich, da gibt's nur eine einzige Seite, die die Antwort dafür liefert und ist das LLM in der Lage, das rauszufinden? Das ist son sehr, sehr tricky Benchmark. Mhm.\n\nSpeaker 1: Wo ich auch kurz den Sinn hinterfragen würde son bisschen, weil wenn ich gesagt hab, wenn jetzt nur einige eine einzige Quelle dafür gibt, so ist das wissen wir, wenn ich irritiert.\n\nSpeaker 3: Ja, weil das die Challenge ist, ist es wirklich in der Lage, diese eine Quelle zu identifizieren? Und tatsächlich ist es sehr interessant gewesen, Opus 4 Punkt 6 ist das erste Model gewesen. Das hat identifiziert, dass es sich grade in der Evaluation befindet. Mhm. Es hat nämlich gesagt, dass man kann ja dann den Thinking Step sehen.\n\nMhm. Hat gesehen, Moment, der User hat mich das gefragt, bla bla fing an, dann erst mal sonen Weg zu finden. Und so mittendrin war in dem Thinking Step dann, das ist eine sehr, sehr spezifische Frage. Die ist so hochspezifisch, befinde ich mich eventuell grade in einem Test. Moment, ich recherchier das kurz.\n\nHat dann angefangen, diese Art von Fragen, die ihm gestellt wurde, im Internet zu suchen, ist in 'nem gelandet, hat rausgefunden, in welchem Benchmark es sich grade befindet, auf Basis auch der vorangegangenen Fragen, konnte aber die Antwort nicht sehen, die sind nämlich encodet. Mhm. Hat sich dann das angeguckt, hat das Encoding geknackt und alle 1200 Antworten richtig submittetet. So, jetzt könnte man sagen, der Benchmark war bestanden, denn es wurden alle 1200 Fragen richtig beantwortet. Mhm.\n\nDie Frage ist, ist der Test jetzt noch valide? Ist, ja, will man jetzt den Output greaten oder will man jetzt den Process greden, ne? Das ist an Wow. Der Stelle schwierig zu sagen. Ja.\n\nIst eine coole Frage. So Ja. Awareness bin ich grade in 1, ja. Ja.\n\nSpeaker 1: Aber ich, also da kam jetzt auch grade dieses Bild, was Du grade beschrieben hast, so voll in Kopf mit dieses, man wirft random Zutaten, holt irgendwie random mitm Ofen und zack, guckt, was dabei rauskommt. Und zufällig ist das geiler als das, was Oma gemacht hat, so. Da denk ich mir so, ja okay, also ist das reproduzierbar grade? Ist das auf alles anwendbar? Und das ist natürlich die Frage, die man sich stellen muss.\n\nAlso deswegen, ja. Ja. Oh, spannend. Sehr gut.\n\nSpeaker 0: Ja klar, wir könnten viel weiterreden, aber irgendwo ist auch der beste Podcast vorbei und auch damit unsere heutige Folge. Von daher liebes Publikum, liebe Hörerinnen und Hörer, wenn ihr Feedback zu dieser Folge habt, dann schreibt uns gerne an Podcast at Programmier Punkt Bar. Vielen Dank an dieser Stelle noch mal an die De Compide vielleicht da draußen, dass wir das im Rahmen machen konnten. Vielen, vielen Dank Martin, dass Du heute Danke, danke. Zeit gefunden hast, mit uns beide zu sprechen.\n\nHat uns sehr viel Spaß gemacht, war sehr informativ und wir machen Heli, Heli, Heli,\n\nSpeaker 1: Heli, Heli, Heli, Heli, Heli, Heli,\n\nSpeaker 3: Heli, Heli,\n\nSpeaker 0: Heli, Heli, Heli, Heli, Heli, Heli, Heli, Heli, Heli, Heli, Heli, Heli, Heli, Heli, Heli, Heli, Heli, Heli, Heli, Heli, Heli, Heli, Heli, Hel Gebäude untergebracht haben, damit wir in Ruhe einen Podcast aufnehmen, 2 Podcasts aufnehmen konnten. Vielen Dank für\n\nSpeaker 2: die Grüße.\n\nSpeaker 3: Macht's gut. Bis bald.\n\nSpeaker 1: Danke, bis dann. Tschau, tschüssi. Tschau.",[26509,26520,26532,26541,26549,26554,26564,26567,26577,26581,26586,26591,26598,26602,26606,26619,26626,26639,26651,26660,26668,26677,26680,26685,26695,26701,26714,26717,26721,26733,26739,26752,26756,26765,26778,26791,26800,26803,26813,26818,26830,26843,26850,26854,26865,26874,26885,26897,26908,26917,26920,26928,26933,26940,26944,26948,26952,26965,26977,26980,26991,27002,27006,27017,27028,27041,27054,27064,27076,27080,27083,27095,27099,27107,27118,27123,27127,27133,27138,27144,27156,27167,27171,27184,27195,27208,27221,27234,27239,27242,27248,27252,27259,27262,27275,27279,27292,27304,27317,27329,27333,27345,27357,27369,27379,27382,27394,27400,27403,27415,27427,27434,27443,27454,27464,27470,27479,27488,27493,27498,27504,27513,27519,27523,27530,27541,27548,27551,27558,27561,27571,27580,27583,27596,27605,27608,27621,27624,27633,27636,27647,27650,27662,27671,27674,27686,27697,27700,27713,27726,27732,27745,27756,27768,27777,27780,27791,27802,27814,27826,27838,27841,27853,27859,27865,27871,27878,27882,27886,27890,27898,27902,27906,27910,27916,27928,27932,27942,27954,27965,27977,27987,27998,28003,28008,28012,28018,28029,28042,28045,28058,28062,28067,28079,28084,28097,28104,28107,28111,28115,28121,28124,28135,28143,28146,28157,28160,28165,28170,28174,28178,28183,28187,28191,28195,28199,28203,28216,28227,28237,28243,28246,28259,28271,28275,28278,28291,28303,28308,28311,28316,28327,28336,28348,28352,28358,28362,28370,28374,28378,28382,28386,28396,28403,28407,28418,28429,28439,28450,28459,28471,28480,28493,28497,28501,28505,28511,28515,28521],{"sentences":26510,"speaker":150,"num_words":795,"start":147,"end":408},[26511,26513,26515,26517,26518],{"text":26512,"start":147,"end":222},"Hallo und herzlich willkommen zu 1 neuen Programmierbar Folge, einem Deep Dive, wo wir heute über AI Evils sprechen.",{"text":26514,"start":227,"end":366},"Das in 'nem besonderen Setting, wir sind nämlich nicht in unserem Studio zu Hause, sondern mit 'ner ganz schönen Aussicht und ich hab gehört auf die Trainingsflächen des, oh, sag schnell, wie der Dresdner Fußballklub heißt.",{"text":26516,"start":366,"end":373},"Dresdner Fußballklub.",{"text":382,"start":378,"end":379},{"text":26519,"start":385,"end":408},"Nee, die haben auch noch 'n Spitznamen.",{"sentences":26521,"speaker":150,"num_words":26531,"start":413,"end":581},[26522,26524,26526,26527,26529],{"text":26523,"start":413,"end":431},"Unser Gast darf man nicht reden.",{"text":26525,"start":431,"end":448},"Oh, der weiß auch nicht.",{"text":454,"start":448,"end":452},{"text":26528,"start":457,"end":471},"Du googelst jetzt parallel.",{"text":26530,"start":471,"end":581},"Anyway, wir gucken hier auf dieses Ding, weil wir sind auf der, 1 coolen Konferenz in Dresden, wo ich letztes Jahr auch schon war und wo ich heute dieses Mal begleitet wurde.",48,{"sentences":26533,"speaker":129,"num_words":26540,"start":581,"end":615},[26534,26536,26537,26539],{"text":26535,"start":581,"end":590},"Dynamo Dresden.",{"text":596,"start":590,"end":594},{"text":26538,"start":594,"end":607},"Kennt man doch.",{"text":26535,"start":607,"end":615},8,{"sentences":26542,"speaker":620,"num_words":26548,"start":615,"end":634},[26543,26544,26545,26546,26547],{"text":596,"start":615,"end":618},{"text":626,"start":618,"end":624},{"text":596,"start":624,"end":628},{"text":626,"start":628,"end":631},{"text":596,"start":631,"end":634},5,{"sentences":26550,"speaker":620,"num_words":935,"start":634,"end":644},[26551,26552,26553],{"text":626,"start":634,"end":638},{"text":596,"start":638,"end":641},{"text":626,"start":641,"end":644},{"sentences":26555,"speaker":129,"num_words":26563,"start":644,"end":684},[26556,26557,26558,26560,26562],{"text":596,"start":644,"end":647},{"text":626,"start":647,"end":651},{"text":26559,"start":651,"end":664},"Dynamo sieht man doch.",{"text":26561,"start":664,"end":679},"Da leuchtet doch irgendwas.",{"text":686,"start":679,"end":684},11,{"sentences":26565,"speaker":620,"num_words":129,"start":684,"end":689},[26566],{"text":692,"start":684,"end":689},{"sentences":26568,"speaker":150,"num_words":26576,"start":689,"end":775},[26569,26570,26571,26573,26575],{"text":698,"start":689,"end":695},{"text":698,"start":700,"end":701},{"text":26572,"start":704,"end":731},"Und wir sitzen hier in 'nem Gebäude von Vodafone.",{"text":26574,"start":731,"end":771},"Vielen Dank an Vodafone, dass wir die diese Räumlichkeiten nutzen dürfen.",{"text":777,"start":771,"end":775},23,{"sentences":26578,"speaker":129,"num_words":620,"start":775,"end":784},[26579],{"text":26580,"start":775,"end":784},"dort Vodafone.",{"sentences":26582,"speaker":150,"num_words":26585,"start":784,"end":810},[26583],{"text":26584,"start":784,"end":810},"Ich bin Dennis Becker, neben mir sitzt",7,{"sentences":26587,"speaker":129,"num_words":26548,"start":813,"end":834},[26588,26590],{"text":26589,"start":813,"end":828},"Dave Acker, Dave Krisczitzky.",{"text":835,"start":833,"end":834},{"sentences":26592,"speaker":150,"num_words":26597,"start":837,"end":930},[26593,26595],{"text":26594,"start":837,"end":918},"Genau und wir haben heute einen Gast eingeladen, der ist Senior Staff AI Engineer und bei Blue Yonder und das ist Martin Seeler.",{"text":26596,"start":922,"end":930},"Herzlich willkommen, Martin.",26,{"sentences":26599,"speaker":935,"num_words":26548,"start":933,"end":947},[26600],{"text":26601,"start":933,"end":947},"Vielen Dank für die Einladung.",{"sentences":26603,"speaker":620,"num_words":620,"start":951,"end":957},[26604],{"text":26605,"start":951,"end":957},"Schön, dass",{"sentences":26607,"speaker":150,"num_words":26618,"start":957,"end":1153},[26608,26610,26612,26614,26616],{"text":26609,"start":957,"end":965},"Du da bist.",{"text":26611,"start":969,"end":1006},"Da ist ja auch schon AI in deinem Titel, den Du den Du trägst.",{"text":26613,"start":1006,"end":1109},"Das heißt, es hört sich so an, als würdest dich schon 'n bisschen tiefer damit auseinandersetzen und jetzt nicht grade erst an der an den Oberflächen kratzen, was das Ganze angeht.",{"text":26615,"start":1114,"end":1142},"Aber erzähl uns gern 'n bisschen was über dich erst mal.",{"text":26617,"start":1142,"end":1153},"Wer bist Du?",62,{"sentences":26620,"speaker":150,"num_words":26625,"start":1153,"end":1190},[26621,26623],{"text":26622,"start":1153,"end":1166},"Wo kommst Du her?",{"text":26624,"start":1166,"end":1190},"Was hast Du so die letzten Jahre gemacht?",12,{"sentences":26627,"speaker":935,"num_words":26638,"start":1194,"end":1484},[26628,26630,26632,26634,26636],{"text":26629,"start":1194,"end":1241},"Ja, also ich bin schon seit über 15 Jahren Softwareentwickler in allen möglichen Bereichen.",{"text":26631,"start":1245,"end":1282},"Und jetzt seit etwas über einem Jahr bin ich bei 'ner Firma namens Blue Jonne.",{"text":26633,"start":1286,"end":1407},"Die kannte ich übrigens auch vorher nicht, also falls es dir genauso geht, die machen Supply Chain Management, alles, was drum herum gehört, Warehouse Management, Transportation Management und von End to End wirklich alles mit dabei.",{"text":26635,"start":1410,"end":1451},"Und meine Rolle ist Senior Star of Engineering, genau, im Bereich AI.",{"text":26637,"start":1451,"end":1484},"Das heißt, ich bin in 'nem Team, das nennt sich AI Studio.",89,{"sentences":26640,"speaker":935,"num_words":26650,"start":1488,"end":1729},[26641,26643,26645,26647,26649],{"text":26642,"start":1488,"end":1556},"Wir sind so das AI Frontier Lab bei Blue Hyundai und tatsächlich sind unsere Hauptaufgaben Post Training von Models.",{"text":26644,"start":1560,"end":1605},"Wir bauen Agents, machen supervist Feintuning, Reinforcement Learning, wir bauen die ganze Infrastruktur.",{"text":26646,"start":1609,"end":1665},"Also wir arbeiten wirklich an den neuesten Agents, die in den nächsten Jahren dann die autonome supply Chain bedienen sollen.",{"text":26648,"start":1665,"end":1725},"Also ich würde schon sagen, cooles Zeug, womit man sich auseinandersetzt, macht auf jeden Fall eine Menge Spaß.",{"text":1731,"start":1725,"end":1729},71,{"sentences":26652,"speaker":150,"num_words":26659,"start":1733,"end":1793},[26653,26655,26657],{"text":26654,"start":1733,"end":1738},"2 Fragen.",{"text":26656,"start":1742,"end":1771},"Ey, i-Studio, wart ihr vor Google oder danach?",{"text":26658,"start":1771,"end":1793},"Wann habt ihr das bitte benannt?",16,{"sentences":26661,"speaker":935,"num_words":26667,"start":1797,"end":1854},[26662,26664,26666],{"text":26663,"start":1797,"end":1815},"Ja, kann ich dir nicht genau beantworten.",{"text":26665,"start":1819,"end":1849},"Stand auf einmal so bei uns auf dem Team auf einmal drauf.",{"text":454,"start":1853,"end":1854},20,{"sentences":26669,"speaker":150,"num_words":26676,"start":1857,"end":1996},[26670,26672,26674],{"text":26671,"start":1857,"end":1957},"Und Supply Chain denkt man ja irgendwie im Tech Bereich erst mal an wahrscheinlich was anderes, also ne, das irgendwie bedeutet irgendwie das Gleiche, aber es ist ja dann irgendwie Software und wird irgendwie reingepflanzt.",{"text":26673,"start":1961,"end":1979},"Aber bei euch geht es Hardware, ne.",{"text":26675,"start":1979,"end":1996},"Also das heißt Transport von",47,{"sentences":26678,"speaker":935,"num_words":129,"start":1998,"end":1999},[26679],{"text":692,"start":1998,"end":1999},{"sentences":26681,"speaker":150,"num_words":26684,"start":2004,"end":2067},[26682],{"text":26683,"start":2004,"end":2067},"Echten Waren von von echten Mitteln, also Sachen, die tatsächlich auf der Welt passieren und nicht nur in einzelnen Nullen ausgedrückt werden.",22,{"sentences":26686,"speaker":935,"num_words":26694,"start":2067,"end":2213},[26687,26688,26690,26692],{"text":2074,"start":2067,"end":2071},{"text":26689,"start":2071,"end":2151},"Also bei uns geht's darum, wenn mal wieder 'n Tanker im Suezkanal stecken bleibt, wie können wir die ganzen Güter von a nach b durchrauten?",{"text":26691,"start":2155,"end":2167},"Wie hilft dir Software dabei?",{"text":26693,"start":2172,"end":2213},"Bis hin zum Return Management, wenn der Kunde wieder Waren zurückgeben würde, genau.",44,{"sentences":26696,"speaker":150,"num_words":26700,"start":2216,"end":2236},[26697,26698],{"text":454,"start":2216,"end":2217},{"text":26699,"start":2221,"end":2236},"Wie definierst Du AI evals?",6,{"sentences":26702,"speaker":935,"num_words":26713,"start":2241,"end":2472},[26703,26705,26707,26709,26711],{"text":26704,"start":2241,"end":2251},"Sehr breiter Begriff.",{"text":26706,"start":2254,"end":2266},"Also das umfasst erst mal alles.",{"text":26708,"start":2270,"end":2334},"Der Name selber ist ja nur die Kurzform von Evaluations, was erst mal alles mit dem Thema absichern, ja, bedeuten kann.",{"text":26710,"start":2334,"end":2416},"Es geht eben darum, sicherzustellen, dass Du deine Core Value Chain sicherst, dass Du beweisen kannst, dass alles so funktioniert, wie Du dir das vorstellst.",{"text":26712,"start":2420,"end":2472},"Auf welche Art und Weise das passiert, das ist abhängig vom Kontext, was Du gerade machen willst.",72,{"sentences":26715,"speaker":620,"num_words":129,"start":2477,"end":2478},[26716],{"text":2480,"start":2477,"end":2478},{"sentences":26718,"speaker":150,"num_words":26597,"start":2482,"end":2559},[26719],{"text":26720,"start":2482,"end":2559},"Und jetzt haben wir heute, also AI ja davorstehen und ich antizipiere irgendwie, eine der großen Schwierigkeiten ist vermutlich, dass es nicht mehr so deterministisch ist,",{"sentences":26722,"speaker":935,"num_words":26732,"start":2559,"end":2853},[26723,26725,26726,26728,26730],{"text":26724,"start":2559,"end":2567},"wie vorher?",{"text":2074,"start":2571,"end":2572},{"text":26727,"start":2576,"end":2654},"Also im klassischen Software Development Prozess hast Du den Vorteil, dass Du 'n Test schreiben kannst, 2 plus 2 ist 4.",{"text":26729,"start":2659,"end":2753},"Sobald Propablistic Output in deiner Anwendung vorkommt, Du also, sagen wir mal, 'n hast wie 'n Large Language Modell, sehen die Dinge auf einmal etwas komplizierter aus.",{"text":26731,"start":2756,"end":2853},"Du hast den Input 2 plus 2, aber jetzt hast Du den Fall, das LLM antwortet dir mit 4 oder das könnte eine 5 halluzinieren oder es schreibt dir 'n Gedicht über die Nummer 4.",86,{"sentences":26734,"speaker":935,"num_words":26738,"start":2853,"end":2906},[26735,26736],{"text":2480,"start":2853,"end":2856},{"text":26737,"start":2858,"end":2906},"Das heißt, Du hast auf einmal viel mehr Testfälle, die Du abdecken musst und das macht's komplizierter.",18,{"sentences":26740,"speaker":150,"num_words":26751,"start":2910,"end":3278},[26741,26743,26745,26747,26749],{"text":26742,"start":2910,"end":3013},"Also mein Gefühl ist, wenn man sich damit beschäftigt und das, was ihr wahrscheinlich auch irgendwie schon implementiert, dann hat man sich ja auch schon 'n bisschen damit beschäftigt und versucht irgendwie, das Ganze unter unter einen Hut zu bringen.",{"text":26744,"start":3016,"end":3054},"Was hast Du fürn Eindruck, wie das aktuell da draußen gehandelt wird, diese Problematik?",{"text":26746,"start":3054,"end":3113},"Also ist ja, in der Regel sprechen wir jetzt erst mal davon, okay, AI wird in irgendwelchen Produkten eingesetzt, ne.",{"text":26748,"start":3113,"end":3244},"Es ist jetzt nicht nur AI in unserem Entwicklungsprozess irgendwie mit drin, wo wir's ja selbst 'ner gewissen Art evaluieren oder Code Reviews machen oder keine Ahnung was, sondern wir haben auf einmal ein Stück Software, das wir auch ausliefern und das auch Endkunden irgendwie nutzen.",{"text":26750,"start":3248,"end":3278},"Und da ist jetzt ein Part drin, der nicht so steuerbar ist",132,{"sentences":26753,"speaker":620,"num_words":620,"start":3278,"end":3283},[26754],{"text":26755,"start":3278,"end":3283},"und vermutlich",{"sentences":26757,"speaker":150,"num_words":26764,"start":3285,"end":3428},[26758,26760,26762],{"text":26759,"start":3285,"end":3320},"ja auch irgendwie halt auch dieses Update Zeug 'n Riesenthema ist, oder?",{"text":26761,"start":3320,"end":3383},"Also ich mein, das, keine Ahnung, es entwickelt sich irgendwie so schnell weiter, dass wir alle x Wochen irgendwie neue Modelle haben.",{"text":26763,"start":3387,"end":3428},"Und da ist auch ein Part davon, dass man irgendwie da vorbereitet ist und das irgendwie abbilden kann?",52,{"sentences":26766,"speaker":935,"num_words":26777,"start":3428,"end":3722},[26767,26769,26771,26773,26775],{"text":26768,"start":3428,"end":3523},"Genau, also wenn wir jetzt von EVIS sprechen und von dem Testen von solchen JennAI Produkten, dann geht's immer darum, Wege zu finden, zum einen die richtigen Tests zu schreiben.",{"text":26770,"start":3527,"end":3546},"Also was genau sind denn die richtigen Tests?",{"text":26772,"start":3546,"end":3569},"Das ist nämlich gar nicht so leicht zu beantworten.",{"text":26774,"start":3571,"end":3656},"Und mithilfe von diesen Tests dann auch Ableitungen zu machen, zum Beispiel über die Qualität deiner Software, dem Ganzen eine Metrik zu vergeben auf Basis von deinen Tests.",{"text":26776,"start":3660,"end":3722},"Und dann, ähnlich wie Du gesagt hast, es entwickelt sich alles rasend schnell weiter und immer wieder kommt ein neues Model raus.",97,{"sentences":26779,"speaker":935,"num_words":26790,"start":3725,"end":3957},[26780,26782,26784,26786,26788],{"text":26781,"start":3725,"end":3796},"Du brauchst eine Art Test, der dir sagt, okay, es ist gut genug, wir können dieses neue Modell mit unserem Produkt ausliefern, das möglichst schnell bei.",{"text":26783,"start":3800,"end":3845},"Kunden sind dann auch hyped, die wollen dann auch das neue Model sehen und in deinem Produkt mit verwenden.",{"text":26785,"start":3849,"end":3889},"Und das gilt nicht nur für deine Kunden, sondern das gilt auch für deine eigenen Entwickler.",{"text":26787,"start":3893,"end":3934},"Die sind genauso hyped, die lesen, oh, hier gibt's neue Technologien, das will ich ausprobieren.",{"text":26789,"start":3934,"end":3957},"Hier gibt's neues Framework, das will ich ausprobieren.",84,{"sentences":26792,"speaker":935,"num_words":26799,"start":3959,"end":4096},[26793,26795,26797],{"text":26794,"start":3959,"end":3998},"Also im besten Fall helfen dir diese EVIS dann auch noch, den richtigen Weg einzuschlagen.",{"text":26796,"start":4002,"end":4023},"Auf was solltest Du dich denn aktuell auch konzentrieren?",{"text":26798,"start":4027,"end":4096},"Also das sind alles Themen, wenn man jeweils richtig umsetzt und da gibt's sehr einfache Prozesse, auf die werden wir wahrscheinlich dann gleich noch eingehen.",49,{"sentences":26801,"speaker":620,"num_words":129,"start":4096,"end":4099},[26802],{"text":2480,"start":4096,"end":4099},{"sentences":26804,"speaker":935,"num_words":26812,"start":4102,"end":4336},[26805,26807,26809,26810],{"text":26806,"start":4102,"end":4151},"Wenn man das richtig umsetzt, dann helfen dir diese Prozesse dabei, all diese Fragen mit einem Schlag zu beantworten.",{"text":26808,"start":4155,"end":4235},"Wohingegen Du auf der anderen Seite häufig diesen sogenannten Vibe Check machst, kommt 'n neues Model raus, Du änderst vielleicht dein Model mal auf die neue Version.",{"text":2480,"start":4235,"end":4238},{"text":26811,"start":4238,"end":4336},"Du lässt deine Anwendungen laufen und ja, schaust einfach nur, ja, fühlt sich gut an und wir releasen das und dann verschreckst Du aber den Kunden und das kostet dich im Endeffekt viel mehr Geld.",82,{"sentences":26814,"speaker":150,"num_words":26817,"start":4339,"end":4442},[26815],{"text":26816,"start":4339,"end":4442},"Und ist dann zwangsweise aber damit auch verbunden, dass wir für diesen Schritt dann auch AI nutzen oder ist es da irgendwie besser, wenn man da wieder auf deterministische Systeme irgendwie zurückfällt oder geht das vielleicht gar nicht?",38,{"sentences":26819,"speaker":935,"num_words":26829,"start":4442,"end":4545},[26820,26822,26824,26826,26828],{"text":26821,"start":4442,"end":4453},"Das geht sehr wohl.",{"text":26823,"start":4456,"end":4473},"Es gibt 3 Blaue Frage.",{"text":26825,"start":4477,"end":4525},"Es gibt 3 3 Wege, wie man das Ganze testet oder man kann das in 3 Kategorien unterteilen.",{"text":26827,"start":4528,"end":4535},"Du kannst machen.",{"text":26827,"start":4539,"end":4545},33,{"sentences":26831,"speaker":935,"num_words":26842,"start":4545,"end":4780},[26832,26834,26836,26838,26840],{"text":26833,"start":4545,"end":4603},"Das, was Du angesprochen hast mit, Du lässt 'n lat Ranguage Model zum Beispiel dir dabei helfen bei der Evaluierung.",{"text":26835,"start":4607,"end":4650},"Und der letzte Schritt, der wird häufig ganz außen vor gelassen, ist Human based Evaluations.",{"text":26837,"start":4654,"end":4705},"Du setzt einfach einen Subject matter Expert an deinen Output, an deiner Anwendung, der das Ganze validiert.",{"text":26839,"start":4709,"end":4736},"Alle Varianten haben ihre Vor- und Nachteile, ne.",{"text":26841,"start":4739,"end":4780},"Also Du kannst, sagen wir mal, Du hast ein Report und Du willst die Qualität testen.",76,{"sentences":26844,"speaker":935,"num_words":26849,"start":4784,"end":4874},[26845,26847],{"text":26846,"start":4784,"end":4824},"Dann kannst Du mit sehr leicht einfache Dinge testen, zum Beispiel die Länge deines Reports.",{"text":26848,"start":4827,"end":4874},"Wenn Du sicherstellen willst, dass dein Report nicht länger als 500 Zeichen ist, kannst Du das ganz einfach testen.",34,{"sentences":26851,"speaker":620,"num_words":620,"start":4874,"end":4879},[26852],{"text":26853,"start":4874,"end":4879},"Das kannst",{"sentences":26855,"speaker":935,"num_words":26817,"start":4879,"end":4982},[26856,26858,26859,26861,26863],{"text":26857,"start":4879,"end":4888},"Du mit Code testen.",{"text":2480,"start":4888,"end":4890},{"text":26860,"start":4890,"end":4902},"Das lässt sich verifizieren.",{"text":26862,"start":4905,"end":4965},"Willst Du, dass deine Zahlen, die Du verwendest, in deinem Report immer mit Metriken versehen sind, kannst Du das mit 1 testen.",{"text":26864,"start":4968,"end":4982},"Das kannst Du auch über Code lösen.",{"sentences":26866,"speaker":935,"num_words":26873,"start":4985,"end":5101},[26867,26869,26871],{"text":26868,"start":4985,"end":5038},"Wenn Du allerdings auf Sachen eingehst, die subjektiv sind, kannst Du das nicht mehr über abbilden, nicht mehr über Code.",{"text":26870,"start":5038,"end":5080},"Also kannst Du entweder über Model based Evaluations gehen oder über Human based Evaluations.",{"text":26872,"start":5083,"end":5101},"Und dann kannst Du dort diese Tests durchführen.",42,{"sentences":26875,"speaker":150,"num_words":26884,"start":5104,"end":5309},[26876,26878,26880,26882],{"text":26877,"start":5104,"end":5120},"Oh, ist das irgendwie son Ding?",{"text":26879,"start":5120,"end":5194},"Ich hab schon gefühlt hier und da gelesen, oder ich weiß nicht, ob das nur für das Code Review gilt, aber vielleicht geht das ja vielleicht als ähnlich.",{"text":26881,"start":5199,"end":5266},"Nimmt man dann die gleichen Modelle oder ist es irgendwie besonders gut, andere zu nehmen, weil die dann nicht den, weiß ich nicht, gleichen haben?",{"text":26883,"start":5266,"end":5309},"Oder ist das am Ende eigentlich egal, weil's 'n sehr anderer Schritt ist, sehr passiert?",74,{"sentences":26886,"speaker":935,"num_words":26896,"start":5313,"end":5481},[26887,26889,26891,26892,26894],{"text":26888,"start":5313,"end":5319},"Eher letzteres.",{"text":26890,"start":5323,"end":5373},"Also wenn Du dir die Aufgabe deiner Anwendungen anschaust, die nehmen wir jetzt als Beispiel 'n LLM.",{"text":2480,"start":5376,"end":5377},{"text":26893,"start":5379,"end":5435},"Das bekommt irgendeine Aufgabe basierend auf diesem User Input, bestimmte Schritte zu absolvieren und dann einen Output zu generieren.",{"text":26895,"start":5438,"end":5481},"Das ist im Vergleich zu einem Test ein ganz anderer Task, der dort absolviert werden muss.",55,{"sentences":26898,"speaker":935,"num_words":731,"start":5485,"end":5660},[26899,26901,26903,26905,26907],{"text":26900,"start":5485,"end":5539},"Und ist in der Regel kein Problem, wenn Du das gleiche Model für deine i-Wis verwendest als für deine normale Anwendung.",{"text":26902,"start":5542,"end":5611},"Es gibt den Fall, der betrifft selten, aber kann vorkommen Modelle, dass, wenn ein Model sich selber Judged, dass es das erkennt, Mhm.",{"text":26904,"start":5613,"end":5639},"Dass der Output von sich, von dem eigenen Model kommt Mhm.",{"text":26906,"start":5641,"end":5656},"Und 'n gewissen Buyes aufweist.",{"text":2480,"start":5659,"end":5660},{"sentences":26909,"speaker":935,"num_words":26916,"start":5663,"end":5774},[26910,26912,26914],{"text":26911,"start":5663,"end":5691},"Das ist aber auch sehr schwer rauszufinden und tritt sehr selten auf.",{"text":26913,"start":5691,"end":5724},"Also ist nichts, was man von Anfang an jetzt mitberücksichtigen muss.",{"text":26915,"start":5726,"end":5774},"'N guter Ansatz ist immer, fang mit dem mächtigsten Model an, was dir zur Verfügung steht.",39,{"sentences":26918,"speaker":620,"num_words":129,"start":5777,"end":5778},[26919],{"text":2480,"start":5777,"end":5778},{"sentences":26921,"speaker":935,"num_words":26618,"start":5778,"end":5949},[26922,26924,26926],{"text":26923,"start":5778,"end":5812},"Und Du kannst später immer noch für Kosten optimieren und andere Modelle ausprobieren.",{"text":26925,"start":5815,"end":5853},"Wichtig ist, dass das Model beim Evaluieren dem Human Judgement sehr nahekommt.",{"text":26927,"start":5857,"end":5949},"Und da sind wir schon in dem Bereich, auch in der in dem Bereich, in dem Prozess, den wir dann gleich vielleicht genauer beschreiben, wie kann ich denn sicherstellen, dass so ein Model da tatsächlich dem übereinstimmt?",{"sentences":26929,"speaker":150,"num_words":26700,"start":5953,"end":5971},[26930,26931],{"text":2480,"start":5953,"end":5954},{"text":26932,"start":5957,"end":5971},"Wir hatten, hast Du eingeatmet?",{"sentences":26934,"speaker":129,"num_words":26585,"start":5974,"end":5991},[26935,26937,26938],{"text":26936,"start":5974,"end":5980},"Ich hab eingeatmet.",{"text":454,"start":5980,"end":5983},{"text":26939,"start":5983,"end":5991},"Wie vielleicht mal",{"sentences":26941,"speaker":620,"num_words":26585,"start":5994,"end":6014},[26942],{"text":26943,"start":5994,"end":6014},"Sorry, nee, nee, nee, nee, nee, nee,",{"sentences":26945,"speaker":935,"num_words":26563,"start":6014,"end":6045},[26946],{"text":26947,"start":6014,"end":6045},"nee, nee, nee, nee, nee, nee, nee, nee, nee, nee, nee,",{"sentences":26949,"speaker":620,"num_words":26540,"start":6045,"end":6067},[26950],{"text":26951,"start":6045,"end":6067},"nee, nee, nee, nee, nee, nee, nee, nee,",{"sentences":26953,"speaker":129,"num_words":26964,"start":6067,"end":6289},[26954,26956,26958,26960,26962],{"text":26955,"start":6067,"end":6092},"nee, nee, nee, nee, nee, nee, nee, nee richtig?",{"text":26957,"start":6096,"end":6116},"Sondern das hat noch mal eine zusätzliche Abgrenzung.",{"text":26959,"start":6119,"end":6213},"Also inwiefern würdest Du, also ich kann mir einfach anschauen so, oh, das Modell performt so und so, das ist so und so gut in diesen Cooding Tasks, das ist gut als LLM so.",{"text":26961,"start":6213,"end":6260},"Also da gibt's ja verschiedene Benchmarks, wonach man sich richtig sag ich mal daher hergestellt.",{"text":26963,"start":6264,"end":6289},"Aber wie würdest Du da die jeweils dann noch mal differenzieren?",77,{"sentences":26966,"speaker":935,"num_words":26976,"start":6292,"end":6507},[26967,26969,26970,26972,26974],{"text":26968,"start":6292,"end":6312},"Also Benchmarks sind ein Teil von jeweils.",{"text":2480,"start":6316,"end":6317},{"text":26971,"start":6317,"end":6401},"Denn was Benchmarks betrifft, Du hast eine Reproduzierbarkeit, Du testest immer wieder auf dem gleichen Input oder Du hast immer wieder die gleichen Aufgaben, die einfach von unterschiedlichen Modellen absolviert werden.",{"text":26973,"start":6403,"end":6431},"Das ist ein Teil, eine Subkategorie von diesem Oberbegriff Ivals.",{"text":26975,"start":6435,"end":6507},"Wenn wir jetzt von Ivice reden, reden wir ja häufig von LLM, 1 sehr konkreten Form von Ivice, wenn wir Lodge Language Modelle testen.",73,{"sentences":26978,"speaker":620,"num_words":129,"start":6509,"end":6510},[26979],{"text":2480,"start":6509,"end":6510},{"sentences":26981,"speaker":935,"num_words":26990,"start":6510,"end":6644},[26982,26984,26986,26988,26989],{"text":26983,"start":6510,"end":6539},"Das ist die Form, wo Du ein anderes Modell zum Testen verwendest.",{"text":26985,"start":6539,"end":6603},"Also ich zeige einem Lodge Language Model, mein Output, schau dir doch mal meinen Report an und sag mir, ob hier ein gewisser Fehler vorliegt.",{"text":26987,"start":6607,"end":6636},"Oder sag mir, ob ich auch schön in Gedichtform geantwortet habe.",{"text":2480,"start":6636,"end":6640},{"text":6647,"start":6640,"end":6644},50,{"sentences":26992,"speaker":935,"num_words":26817,"start":6649,"end":6759},[26993,26995,26997,26998,27000],{"text":26994,"start":6649,"end":6671},"Das ist nämlich sehr schwer, als Test einfach abzubilden.",{"text":26996,"start":6675,"end":6689},"Das ist quasi unmöglich.",{"text":2480,"start":6692,"end":6693},{"text":26999,"start":6693,"end":6728},"Also nehme ich mir einfach ein anderes Modell her und geborene ihm diese Aufgabe.",{"text":27001,"start":6728,"end":6759},"Das sind diese, deswegen nennt man das LLM Message Judge.",{"sentences":27003,"speaker":620,"num_words":620,"start":6763,"end":6767},[27004,27005],{"text":2480,"start":6763,"end":6764},{"text":460,"start":6764,"end":6767},{"sentences":27007,"speaker":935,"num_words":26964,"start":6767,"end":6997},[27008,27010,27012,27013,27015],{"text":27009,"start":6767,"end":6798},"lässt ein anderes Lodge Language Modell über deinen Output judgen, bewerten.",{"text":27011,"start":6802,"end":6894},"Und wenn wir jetzt uns das so vorstellen, dass wir auch da ein Dataset haben und immer wieder die gleichen Inputs gegen unterschiedliche Modelle testen, dann könnte das unser ganz persönlicher Benchmark sein.",{"text":2480,"start":6894,"end":6897},{"text":27014,"start":6899,"end":6941},"Wir können aber auch diesen dieses LAM so aufsetzen, dass wir Onlinemonitoring betreiben.",{"text":27016,"start":6945,"end":6997},"Also jedes Mal, wenn ein User mit unserer Anwendung interagiert, wird dieser Judge ausgelöst und evaluiert diesen neuen Output.",{"sentences":27018,"speaker":935,"num_words":27027,"start":7000,"end":7168},[27019,27021,27023,27025,27026],{"text":27020,"start":7000,"end":7056},"Jetzt hab ich kein vordefiniertes Dataset, was immer gleich bleibt, sondern stattdessen ein Judge, der jeden meiner Outputs graded.",{"text":27022,"start":7060,"end":7132},"Genau, aber sind deswegen dieser Oberbegriff, nehm ich immer wieder den gleichen Input und teste das auf verschiedene Modelle, lasse ich immer wieder die gleichen Tests durchlaufen.",{"text":27024,"start":7135,"end":7161},"Dann komme ich zu dieser Reproduzierbarkeit und lande bei Benchmarks.",{"text":2480,"start":7164,"end":7165},{"text":698,"start":7165,"end":7168},58,{"sentences":27029,"speaker":150,"num_words":27040,"start":7170,"end":7613},[27030,27032,27034,27036,27038],{"text":27031,"start":7170,"end":7210},"Wir haben letztens, auch eigentlich 'n bisschen blauäugig, aber irgendwie in die gleiche Richtung.",{"text":27033,"start":7210,"end":7267},"Also wir haben bei uns sone AI Week gehabt, wo das ganze Unternehmen einfach Zeit hatte, sich damit zu beschäftigen.",{"text":27035,"start":7267,"end":7326},"Und dann sind wir auch viel son bisschen in die anderen Teams und Bereiche, die nicht nur Dev Entwicklungsteams waren, gekommen.",{"text":27037,"start":7329,"end":7549},"Und da hab ich mit HR zusammen sonen, sagen wir mal, Prototypen, Prototypen zusammengehackt mit Cloud Code, wo es darum ging, dass wir für potenzielle Kandidat*innen, die wir im Active Sourcing uns angucken, die ist, ist, glaub ich, dann son bisschen impliziert passiert, was da eigentlich, also was 'n bisschen ein Teil davon ist, weil es gab dann so eine AI, die die Recherche gemacht hat und das Ganze irgendwie gesagt hat, hey, das sind die Sourcen.",{"text":27039,"start":7549,"end":7613},"Und dann haben wir gesagt, na ja, irgendwie stell dir mal sicher, dass die auch relevant sind und dass da wirklich die Person geht.",156,{"sentences":27042,"speaker":150,"num_words":27053,"start":7613,"end":7925},[27043,27045,27047,27049,27051],{"text":27044,"start":7613,"end":7665},"Und da kam dann er halt auch einfach und hat gesagt, ja okay, ich mach jetzt hier son Score hin.",{"text":27046,"start":7665,"end":7748},"Und hat irgendwie schöne Graphen gemalt und gesagt, wie sicher es ist jetzt da, dass es sich die Person handelt und dass es irgendwie relevanter Inhalt ist dafür.",{"text":27048,"start":7752,"end":7810},"Was irgendwie schon ein Teil dann ist oder da wo dann ein LLM irgendwie sagt hat, ja, was wie wie sicher bin ich mir denn?",{"text":27050,"start":7810,"end":7883},"Und da war ich mir total unsicher, ist das, also kann man dem jetzt irgendwie trauen oder ist das auch irgendwie super random, was jetzt da rauskommt?",{"text":27052,"start":7883,"end":7925},"Und die sagt, ja, ich bin mir mal zu 55 Prozent sicher, dass das richtig ist.",116,{"sentences":27055,"speaker":935,"num_words":26884,"start":7929,"end":8135},[27056,27057,27059,27061,27063],{"text":698,"start":7929,"end":7930},{"text":27058,"start":7933,"end":7946},"Ja, also es ist sehr schwer.",{"text":27060,"start":7946,"end":8031},"Das kommt davon, kommt darauf an, ob man das als Teil seiner Value Chain mit betrachtet, diese Bewertung von diesem anderen Modell oder ob man das Teil seines Testings betrachtet.",{"text":27062,"start":8034,"end":8132},"Also wenn Du das jetzt als Teil der Core Anwendungen mitsiehst, würde ich eher sagen, handelt sich ein Multi Agent System, wo Du mehrere Large Language Models zusammenwendest, die unterschiedliche Aufgaben haben und die Sache zusammenbringen.",{"text":2480,"start":8132,"end":8135},{"sentences":27065,"speaker":935,"num_words":27075,"start":8135,"end":8359},[27066,27068,27070,27071,27073],{"text":27067,"start":8135,"end":8201},"Wenn Du jetzt aber sagst, okay, das ist nur für uns zum Validieren, dann würde ich schon durchaus sagen, das geht schon in die Richtung Testing.",{"text":27069,"start":8205,"end":8241},"Aber das ist ja das Schwierige, die Frage, was sind denn die die richtigen Tests?",{"text":2480,"start":8243,"end":8244},{"text":27072,"start":8247,"end":8322},"Das ist sehr schwierig und ja, da da scheitern die meisten dran, denn es gibt sehr viele vordefinierte Ivals, die kann man einfach so installieren.",{"text":27074,"start":8326,"end":8359},"Du hast dann ganz tolle Dashboards und Metriken, die dir dann angezeigt werden.",80,{"sentences":27077,"speaker":935,"num_words":26659,"start":8359,"end":8414},[27078],{"text":27079,"start":8359,"end":8414},"Da steht dann eine Zahl drin, Helpfulness oder Trustwürphy und irgendeinen Wert zwischen 1 und 5.",{"sentences":27081,"speaker":620,"num_words":129,"start":8414,"end":8417},[27082],{"text":2480,"start":8414,"end":8417},{"sentences":27084,"speaker":935,"num_words":27094,"start":8420,"end":8605},[27085,27086,27088,27090,27092],{"text":8424,"start":8420,"end":8421},{"text":27087,"start":8426,"end":8540},"Wenn ich jetzt eine Anwendung schreibe oder 'n Produktentwickler und meinem Boss sage, also wir haben hier ein Produkt und das hat einen Helpfunes Score von 4.2, dann guckt der mich mit runzelnder Stirn an und denkt wahrscheinlich, dass ich verrückt bin.",{"text":27089,"start":8543,"end":8564},"Was was soll er dem das jetzt sagen?",{"text":27091,"start":8564,"end":8579},"Also ist das jetzt gut genug?",{"text":27093,"start":8579,"end":8605},"Können wir das jetzt auf 10000 Kunden losgehen lassen?",66,{"sentences":27096,"speaker":935,"num_words":26563,"start":8605,"end":8639},[27097],{"text":27098,"start":8605,"end":8639},"Ja oder nein, das ist, das sagt dem gar nix, also",{"sentences":27100,"speaker":129,"num_words":26659,"start":8641,"end":8681},[27101,27103,27105],{"text":27102,"start":8641,"end":8654},"Oder warum ist es nicht 5?",{"text":27104,"start":8654,"end":8666},"Das wär eigentlich meine Frage.",{"text":27106,"start":8666,"end":8681},"Alles sollte doch top sein.",{"sentences":27108,"speaker":935,"num_words":26896,"start":8681,"end":8824},[27109,27110,27112,27114,27116],{"text":692,"start":8681,"end":8683},{"text":27111,"start":8687,"end":8738},"Ja, also das ist die schwierige Frage und darum ging's ja auch heute hier auf der Konferenz son bisschen in dem Talk.",{"text":27113,"start":8742,"end":8762},"Wie schreibt man denn eigentlich richtig gute jeweils?",{"text":27115,"start":8762,"end":8790},"Wie wie kommt man an den Punkt, dass man das versteht?",{"text":27117,"start":8790,"end":8824},"Und wie nutzt man dann diese jeweils, die eben in in Metriken umzuwandeln?",{"sentences":27119,"speaker":150,"num_words":27122,"start":8828,"end":8837},[27120],{"text":27121,"start":8828,"end":8837},"Und wie macht man's?",4,{"sentences":27124,"speaker":935,"num_words":620,"start":8840,"end":8847},[27125,27126],{"text":698,"start":8840,"end":8841},{"text":8849,"start":8846,"end":8847},{"sentences":27128,"speaker":129,"num_words":26548,"start":8851,"end":8866},[27129,27131],{"text":27130,"start":8851,"end":8856},"guter Übergang.",{"text":27132,"start":8860,"end":8866},"Sehr gute Frage.",{"sentences":27134,"speaker":935,"num_words":27137,"start":8868,"end":8929},[27135],{"text":27136,"start":8868,"end":8929},"Es ist es ist eigentlich relativ leicht, es ist so leicht, dass es schon absurd ist, dass es nicht einfach jeder so macht, aber",24,{"sentences":27139,"speaker":129,"num_words":26563,"start":8933,"end":8967},[27140,27142],{"text":27141,"start":8933,"end":8940},"Spannend, okay.",{"text":27143,"start":8940,"end":8967},"Ja, also ich bin wirklich auf die Antwort gespannt.",{"sentences":27145,"speaker":935,"num_words":26713,"start":8967,"end":9166},[27146,27148,27150,27152,27154],{"text":27147,"start":8967,"end":9011},"Ja, also es ist tatsächlich so, es ist nicht mal was Neues, was sich jetzt jemand ausgedacht hat.",{"text":27149,"start":9011,"end":9068},"Es ist etwas, das schon seit zig Jahren existiert, ein Begriff, der auch so trivial klingt, nennt sich Error Analysis.",{"text":27151,"start":9072,"end":9089},"Und der besteht aus ganz wenigen Schritten.",{"text":27153,"start":9093,"end":9124},"Schritt Nummer 1, Du sammelst dir Daten aus der aus deiner Anwendung.",{"text":27155,"start":9127,"end":9166},"Du richtest irgend eine Art von Monitoring ein, erst mal die ganzen Daten zu sammeln.",{"sentences":27157,"speaker":935,"num_words":27166,"start":9170,"end":9249},[27158,27160,27162,27164],{"text":27159,"start":9170,"end":9192},"Jede Interaktion von deinem Customer mit deinem Produkt.",{"text":27161,"start":9195,"end":9223},"Und Schritt Nummer 2 ist, Du schaust dir deine Daten tatsächlich einfach mal an.",{"text":27163,"start":9227,"end":9237},"Also ganz, ganz trivial.",{"text":27165,"start":9240,"end":9249},"Wer hätte das gedacht?",30,{"sentences":27168,"speaker":129,"num_words":26548,"start":9249,"end":9263},[27169],{"text":27170,"start":9249,"end":9263},"Ja, bisher bisher wirklich einfach.",{"sentences":27172,"speaker":935,"num_words":27183,"start":9263,"end":9525},[27173,27175,27177,27179,27181],{"text":27174,"start":9263,"end":9285},"Ja, Du schaust dir deine Daten einfach mal an.",{"text":27176,"start":9285,"end":9323},"Und zwar in dem ersten Schritt nimmst Du die Rolle 1 Customers ein.",{"text":27178,"start":9326,"end":9398},"Du bist im besten Fall jemand, wenn Du die Kommentare drinschreibst, der diese Produktidee hat, der bewerten kann, was was genau heißt denn gut aus Customer Sicht?",{"text":27180,"start":9402,"end":9432},"Und Du nimmst seine Rolle ein und schaust dir deinen Output einfach mal an.",{"text":27182,"start":9434,"end":9525},"Und ich kann ja mal ein Beispiel bringen, bei uns bei Blöhrender, wir haben natürlich viele Projekte, 1 ist auch so ein Report für Warehouse Manager, wo wir alle möglichen und Probleme aufdecken.",96,{"sentences":27185,"speaker":935,"num_words":26799,"start":9528,"end":9665},[27186,27188,27189,27191,27193],{"text":27187,"start":9528,"end":9569},"Und unser Report enthielt dann Daten zum Beispiel zu Verspätungen bei Shipments, Late Shipments.",{"text":2480,"start":9573,"end":9574},{"text":27190,"start":9576,"end":9628},"Der erste Satz war gleich, wir haben 100 Prozent Late Shipments und das basiert auf 0 Late Shipments, so.",{"text":27192,"start":9628,"end":9640},"Also wir haben keine Late Shipments.",{"text":27194,"start":9643,"end":9665},"Es gibt keine, aber wir reporten irgendwie 100 Prozent.",{"sentences":27196,"speaker":935,"num_words":27207,"start":9668,"end":9793},[27197,27199,27201,27203,27205],{"text":27198,"start":9668,"end":9680},"Das macht natürlich keinen Sinn.",{"text":27200,"start":9683,"end":9721},"So, also hinterlässt man einfach nur den Kommentar, dass es hier eine Diskrepanz gibt.",{"text":27202,"start":9721,"end":9729},"Ganz, ganz trivial.",{"text":27204,"start":9729,"end":9745},"Einfach nur runterschreiben, mehr nix.",{"text":27206,"start":9748,"end":9793},"In dem gleichen Report haben wir auch noch davon gesprochen, von Aber wir hatten ja gar keine Late Shipments.",46,{"sentences":27209,"speaker":935,"num_words":27220,"start":9795,"end":9957},[27210,27212,27214,27216,27218],{"text":27211,"start":9795,"end":9807},"Also das ist totale Übertreibung.",{"text":27213,"start":9811,"end":9826},"Dann mach ich mir genauso 'n Kommentar.",{"text":27215,"start":9830,"end":9887},"Hier wurde übertrieben und ohne eine konkrete Zahl, das ist ja auch noch mit dabei, was bedeutet denn in dem Kontext, Mhm.",{"text":27217,"start":9889,"end":9903},"Auf einen Missstand hingewiesen.",{"text":27219,"start":9907,"end":9957},"Noch viel schlimmer, wir haben dann bewertet, wie gibt es Probleme mit den Leuten, die im Warehouse arbeiten?",56,{"sentences":27222,"speaker":935,"num_words":27233,"start":9961,"end":10220},[27223,27225,27227,27229,27231],{"text":27224,"start":9961,"end":10000},"Und dann standen da so Sachen drin wie, Low Performers, das ist halt kritisch.",{"text":27226,"start":10004,"end":10118},"Also wenn ich ein Produkt rausgebe an Tausende von Kunden mit jeweils 10000 Warehouse Mitarbeitern und ich nenn die alle Low Performer, dann hab ich wahrscheinlich am nächsten Tag von HR einen Brief in meinem Postfach, dass die mich zum Gespräch einladen.",{"text":27228,"start":10121,"end":10167},"Also hinterlasse ich auch da einen Kommentar, dass wir hier HR sensitive Sprache in unseren Reports verwenden.",{"text":27230,"start":10169,"end":10187},"Ich schreib also alle diese Kommentare runter.",{"text":27232,"start":10187,"end":10220},"Ich geh durch und am besten annotier ich nur diesen ersten Upstreamfehler.",92,{"sentences":27235,"speaker":935,"num_words":27238,"start":10224,"end":10280},[27236],{"text":27237,"start":10224,"end":10280},"Denn alle Sachen, die ich danach entdecke, haben wahrscheinlich mit dem Anfang dem Problem zu tun, ab wo der erste Drift",21,{"sentences":27240,"speaker":620,"num_words":129,"start":10283,"end":10284},[27241],{"text":2480,"start":10283,"end":10284},{"sentences":27243,"speaker":935,"num_words":26576,"start":10288,"end":10353},[27244,27246],{"text":27245,"start":10288,"end":10293},"Abgegangen ist.",{"text":27247,"start":10295,"end":10353},"Und das reicht aus tatsächlich, man könnte jetzt meinen, oh, da muss ich ja Tausende mir solcher Traces angucken, aber nein.",{"sentences":27249,"speaker":129,"num_words":26700,"start":10353,"end":10371},[27250],{"text":27251,"start":10353,"end":10371},"Das war mein erster Gedanke, ja.",{"sentences":27253,"speaker":935,"num_words":27258,"start":10371,"end":10418},[27254,27256],{"text":27255,"start":10371,"end":10386},"Tatsächlich musst Du das nicht.",{"text":27257,"start":10386,"end":10418},"Du kannst sehr, sehr leicht anfangen mit ganz wenigen, beispielsweise 20 Traces.",17,{"sentences":27260,"speaker":620,"num_words":129,"start":10422,"end":10423},[27261],{"text":2480,"start":10422,"end":10423},{"sentences":27263,"speaker":935,"num_words":27274,"start":10426,"end":10645},[27264,27266,27268,27270,27272],{"text":27265,"start":10426,"end":10477},"Du gehst einfach dadurch, das dauert vielleicht 2 Stunden, 3 Stunden und Du hast mindestens 100 Kommentare gesammelt.",{"text":27267,"start":10481,"end":10525},"Und zwar Kommentare, die perfekt auf dein Produkt zugeschnitten sind, die Fehler in deiner Anwendung aufzeigen.",{"text":27269,"start":10529,"end":10580},"Denn es ist sehr leicht für jemanden im Produktmanagement sich zu überlegen, was möchte ich denn an Output sehen?",{"text":27271,"start":10584,"end":10632},"Aber auf der anderen Seite ist es sehr schwer, im Vornherein darüber nachzudenken, was soll es denn nicht sagen?",{"text":27273,"start":10632,"end":10645},"Was möchte ich denn nicht sehen?",78,{"sentences":27276,"speaker":129,"num_words":620,"start":10645,"end":10651},[27277,27278],{"text":2480,"start":10645,"end":10647},{"text":706,"start":10647,"end":10651},{"sentences":27280,"speaker":935,"num_words":27291,"start":10651,"end":10811},[27281,27283,27285,27287,27289],{"text":27282,"start":10651,"end":10680},"das sehe ich natürlich nur, wenn ich mir die Daten tatsächlich angucke.",{"text":27284,"start":10683,"end":10744},"Also wir haben dann, insgesamt haben wir 8, 87 Reports uns angeguckt, sind da durchgegangen und hatten etwas über 160 Kommentare zusammengesammelt.",{"text":27286,"start":10748,"end":10756},"Dann haben wir aufgehört.",{"text":27288,"start":10759,"end":10767},"Warum haben wir aufgehört?",{"text":27290,"start":10770,"end":10811},"Wir haben einen Punkt erreicht, den nennt man Theoratical Saturation, theoretische Sättigung.",54,{"sentences":27293,"speaker":935,"num_words":27303,"start":10815,"end":10915},[27294,27295,27297,27299,27301],{"text":2480,"start":10815,"end":10816},{"text":27296,"start":10816,"end":10872},"Das ist der Punkt, an dem Du feststellst, hm, ich schreib zwar Kommentare, aber das hab ich alles schon mal hingeschrieben.",{"text":27298,"start":10875,"end":10889},"Irgendwie find ich grade nix Neues.",{"text":27300,"start":10893,"end":10903},"Hier ist eigentlich Schluss.",{"text":27302,"start":10906,"end":10915},"Du kannst hier aufhören.",36,{"sentences":27305,"speaker":935,"num_words":27316,"start":10918,"end":11069},[27306,27308,27310,27312,27314],{"text":27307,"start":10918,"end":10942},"Ab hier kostet dich der Prozess einfach nur noch Geld Ja.",{"text":27309,"start":10944,"end":10962},"Und Du hast sowieso schon sehr viel gefunden.",{"text":27311,"start":10965,"end":10980},"Okay, also das waren die 2 Schritte.",{"text":27313,"start":10980,"end":11045},"Wir müssen erst mal sicherstellen, dass wir die Traces sammeln, diese Logs von unserer Anwendung und dann gehen wir da durch und kommentieren das.",{"text":27315,"start":11048,"end":11069},"So, Schritt 3 ist, Du gruppierst diese ganzen Fehler.",59,{"sentences":27318,"speaker":935,"num_words":26777,"start":11073,"end":11336},[27319,27321,27323,27325,27327],{"text":27320,"start":11073,"end":11102},"Und Du sammelst alle, dafür kannst Du dann tatsächlich wieder ein LLM nehmen.",{"text":27322,"start":11102,"end":11153},"Du könntest alle Kommentare nehmen, die 'n Chat gbt reinschmeißen und sagen, bitte stell mir mal 'n paar Gruppen.",{"text":27324,"start":11157,"end":11230},"Oder Du gehst technischer ran und machst das auf Basis von Keywords oder Embeddings, die zu clustern, kannst Du machen, wie Du das für richtig empfindest.",{"text":27326,"start":11233,"end":11299},"Und in unserem Fall gibt es mehrere HR sensitive Kommentare und wir haben das zusammengefasst zu 1 Gruppe, die umschreibt dieses Problem sehr gut.",{"text":27328,"start":11299,"end":11336},"Und das ist auf einmal ein sehr guter für unsere Anwendung im Vergleich zu Helpfulness.",{"sentences":27330,"speaker":129,"num_words":620,"start":11340,"end":11347},[27331,27332],{"text":2480,"start":11340,"end":11341},{"text":11348,"start":11346,"end":11347},{"sentences":27334,"speaker":935,"num_words":26964,"start":11350,"end":11554},[27335,27337,27339,27341,27343],{"text":27336,"start":11350,"end":11398},"Ja, wenn ich das meinem Boss zeige und dem sage, unser ist dies und das, ist das viel, viel hilfreicher.",{"text":27338,"start":11402,"end":11455},"Diese 168 Kommentare, die wir hatten, haben wir auf 19 Kategorien runtergebrochen, also 19 sogenannte sind dabei rausgekommen.",{"text":27340,"start":11458,"end":11487},"Das sind die Zustände deiner Anwendungen, die Du eigentlich nicht sehen möchtest.",{"text":27342,"start":11491,"end":11536},"Also ja, Du kommst am Ende auf Tests, die genau das testen, was deine Ahnung nicht mehr machen soll.",{"text":27344,"start":11539,"end":11554},"Und das kannst Du auch immer wieder machen.",{"sentences":27346,"speaker":935,"num_words":26618,"start":11556,"end":11734},[27347,27349,27351,27353,27355],{"text":27348,"start":11556,"end":11627},"Es ist ausreichend, dass Du damit einmal anfängst und das alles aufsetzt, aber es wird der Punkt kommen, dass deine Nutzer anders mit deiner Anwendung interagieren.",{"text":27350,"start":11630,"end":11643},"Und bestes Beispiel ist ChatGPT.",{"text":27352,"start":11647,"end":11678},"Wenn Du mal daran denkst, als ChatGPT rauskam, was hat jeder gemacht?",{"text":27354,"start":11681,"end":11705},"Der hat sich irgendwelche lustigen Gedichte erzeugen lassen.",{"text":27356,"start":11708,"end":11734},"Schreib mir mal ein Gedicht über Softwareentwicklung oder was weiß ich.",{"sentences":27358,"speaker":935,"num_words":26976,"start":11738,"end":11938},[27359,27361,27363,27365,27367],{"text":27360,"start":11738,"end":11789},"Heutzutage macht das niemand mehr, das ist langweilig geworden, aber am Anfang haben die Nutzer so mit ChatGPT interagiert.",{"text":27362,"start":11793,"end":11834},"Und das nennt man User Drift, die Nutzer verändern, wie sie mit deiner Anwendungen interagieren.",{"text":27364,"start":11836,"end":11859},"Wenn Du das feststellst, dann kannst Du diesen Prozess wiederholen.",{"text":27366,"start":11859,"end":11903},"Aber erst mal ist das tatsächlich eine Arbeit und Du bist sehr gut abgesichert erst mal.",{"text":27368,"start":11903,"end":11938},"Also unsere 19, die die bleiben ja echt, die bleiben ja auch relevant.",{"sentences":27370,"speaker":935,"num_words":689,"start":11941,"end":12084},[27371,27373,27375,27377],{"text":27372,"start":11941,"end":11977},"In 2 Monaten sag ich ja nicht auf einmal, ist nicht mehr relevant für unsere Anwendung.",{"text":27374,"start":11979,"end":12003},"Das heißt, das kann ich kosteneffizient einfach so umsetzen.",{"text":27376,"start":12006,"end":12058},"Und was Du dann machst, ist, Du Du Du bildest das als Test ab, der die ganze Zeit dann deine Outputs graded.",{"text":27378,"start":12061,"end":12084},"Also da sind wir wieder bei diesem LLM Message Judge.",{"sentences":27380,"speaker":150,"num_words":129,"start":12084,"end":12087},[27381],{"text":2480,"start":12084,"end":12087},{"sentences":27383,"speaker":935,"num_words":26650,"start":12090,"end":12294},[27384,27386,27388,27390,27392],{"text":27385,"start":12090,"end":12107},"Da gibt es ja alle mögliche Software.",{"text":27387,"start":12110,"end":12141},"Wir benutzen zum Beispiel Langviewes und dann schreibst Du da eine prompt.",{"text":27389,"start":12144,"end":12207},"Und in diese prompt schreibst Du rein, hier, das ist ein Report und bitte teste, ob sich hier drin befindet, ja oder nein.",{"text":27391,"start":12209,"end":12285},"Und in diesen in dieser Anfrage kannst Du diesem anderen Luch Language Modell auch Beispiele reingeben, nämlich die Beispiele, die Du in deinen Kommentaren gefunden hast.",{"text":27393,"start":12285,"end":12294},"Ist also ideal.",{"sentences":27395,"speaker":935,"num_words":26684,"start":12297,"end":12349},[27396,27398],{"text":27397,"start":12297,"end":12328},"Wir können also sagen, wenn wir hier von Low Performern reden, ist das schlecht.",{"text":27399,"start":12331,"end":12349},"Können wir dem direkt so als Beispiel geben.",{"sentences":27401,"speaker":620,"num_words":129,"start":12349,"end":12353},[27402],{"text":2480,"start":12349,"end":12353},{"sentences":27404,"speaker":935,"num_words":26916,"start":12356,"end":12457},[27405,27407,27409,27411,27413],{"text":27406,"start":12356,"end":12373},"Und dann schmeißen wir dort den Report rein.",{"text":27408,"start":12376,"end":12404},"Und was rauskommt, ist ein, ja, was was kommt eigentlich raus?",{"text":27410,"start":12404,"end":12420},"Das ist schon das nächste Thema.",{"text":27412,"start":12423,"end":12447},"Häufig wird ein Wert zwischen 1 und 5 erwartet, ne.",{"text":27414,"start":12447,"end":12457},"Wie bei diesen Helphoenes.",{"sentences":27416,"speaker":935,"num_words":27426,"start":12461,"end":12548},[27417,27418,27420,27422,27424],{"text":698,"start":12461,"end":12462},{"text":27419,"start":12462,"end":12470},"1 bis 5.",{"text":27421,"start":12473,"end":12501},"Wenn ich dich jetzt fragen würde, ist das 3.7 oder 8 8.5?",{"text":27423,"start":12501,"end":12511},"Ich hab keine Ahnung.",{"text":27425,"start":12514,"end":12548},"Dann würdest Du dir wahrscheinlich auch die Frage stellen, ja, wo ist denn der Unterschied?",35,{"sentences":27428,"speaker":935,"num_words":26817,"start":12551,"end":12653},[27429,27430,27432],{"text":8424,"start":12551,"end":12552},{"text":27431,"start":12555,"end":12585},"Und das zeigt schon das Problem mit solchen sogenannten Leicad Skalen.",{"text":27433,"start":12588,"end":12653},"Die sind in der Theorie ganz nett, weil Du dieses Gefühl hast, dass Du eine Metrik hast, die sich verbessert, aber die sagt dir eigentlich nix.",{"sentences":27435,"speaker":129,"num_words":26618,"start":12653,"end":12824},[27436,27437,27439,27441],{"text":698,"start":12653,"end":12655},{"text":27438,"start":12655,"end":12674},"Wobei, ich würd sagen, komparativ schon.",{"text":27440,"start":12674,"end":12793},"Also wenn ich weiß, so vorher wurd das auf 8 geschätzt und jetzt hab ich bei Erinnerung gemacht, also auf 4, dann würd ich sagen dann, okay, also ich kann nicht, ich kann mit dem Wert per se nichts anfangen, aber ich weiß es Genau.",{"text":27442,"start":12796,"end":12824},"Scheint irgendwie weniger justmental zu sein, was vielleicht gut ist.",{"sentences":27444,"speaker":935,"num_words":27291,"start":12824,"end":12976},[27445,27446,27448,27450,27452],{"text":698,"start":12824,"end":12826},{"text":27447,"start":12826,"end":12834},"Das stimmt.",{"text":27449,"start":12837,"end":12888},"Auf der anderen Seite, wenn Du einen Wert zwischen, sagen wir mal, 0 und 5 hast, dann passiert was Spannendes.",{"text":27451,"start":12888,"end":12955},"Sagen wir mal, wir haben 100 Outputs oder 100 Reports und wir lassen ein LLM as Judge, unsere 100 Reports grainen nach Judgemental language.",{"text":27453,"start":12955,"end":12976},"0 bedeutet, absolut kein Judgemental language enthalten.",{"sentences":27455,"speaker":935,"num_words":27166,"start":12980,"end":13068},[27456,27458,27460,27461,27463],{"text":27457,"start":12980,"end":12993},"5 ist, wir beschimpfen Ja.",{"text":27459,"start":12995,"end":13014},"Unsere Worker hier von vorne wissen.",{"text":698,"start":13014,"end":13018},{"text":27462,"start":13020,"end":13065},"Und wenn Du jetzt über die 100 Reports einen Durchschnittswert von 0 hättest, veremdest Du das okay?",{"text":13070,"start":13065,"end":13068},{"sentences":27465,"speaker":935,"num_words":26597,"start":13068,"end":13136},[27466,27468],{"text":27467,"start":13068,"end":13088},"Also keiner unserer Reports hat Judment Language.",{"text":27469,"start":13091,"end":13136},"Wenn Du jetzt mein Boss wärst und ich frag dich, können wir das Produkt so rauslassen, würdest Du sagen",{"sentences":27471,"speaker":129,"num_words":27478,"start":13138,"end":13254},[27472,27474,27476],{"text":27473,"start":13138,"end":13163},"Das, also es ist schon als Fangfrage formuliert, so.",{"text":27475,"start":13163,"end":13218},"Ich merk schon, ich laufe eine Falle, aber ich würd erst mal sagen, weil ich geh drauf ein, ja klar.",{"text":27477,"start":13218,"end":13254},"Nur, wir wollen ja gar keine Challenge an Englisch haben, ne.",40,{"sentences":27480,"speaker":935,"num_words":27258,"start":13254,"end":13304},[27481,27483,27485,27487],{"text":27482,"start":13254,"end":13261},"Perfekt, perfekt.",{"text":27484,"start":13261,"end":13277},"Können wir releasen können wir releasen.",{"text":27486,"start":13277,"end":13299},"Was ist denn, wenn unser Durchschnittswert 1 ist?",{"text":13306,"start":13303,"end":13304},{"sentences":27489,"speaker":129,"num_words":27492,"start":13308,"end":13340},[27490],{"text":27491,"start":13308,"end":13340},"Ich will, da würd ich auch noch sagen, das können die Leute ab.",13,{"sentences":27494,"speaker":935,"num_words":27122,"start":13340,"end":13351},[27495,27496],{"text":454,"start":13340,"end":13343},{"text":27497,"start":13343,"end":13351},"Und bei 2?",{"sentences":27499,"speaker":129,"num_words":27478,"start":13354,"end":13467},[27500,27502],{"text":27501,"start":13354,"end":13406},"Ich glaub, ich find bei 5 sogar, ja, sag ich, ich will 'n schlechtes Beispiel dafür, glaub ich.",{"text":27503,"start":13406,"end":13467},"Aber Wär stimmt, aber genau, irgendwann wird man sich, glaub ich, selbst erwarten, 'n Wert zu schreiben, so was ist noch hinnehmbar?",{"sentences":27505,"speaker":935,"num_words":27027,"start":13470,"end":13608},[27506,27507,27509,27511],{"text":692,"start":13470,"end":13471},{"text":27508,"start":13471,"end":13524},"Also Du läufst in die Falle, dass Du aus diesem Problem von, bewerte das mal zwischen 1 bis 5 oder 0 bis 5.",{"text":27510,"start":13526,"end":13579},"Du machst sowieso wieder 'n binäres Problem da draus, denn Du musst an irgend 'nem Punkt sagen, hier ist der Cut off.",{"text":27512,"start":13582,"end":13608},"Ab diesem Wert fühlen wir uns nicht mehr gut, das zu releasen.",{"sentences":27514,"speaker":129,"num_words":27207,"start":13611,"end":13729},[27515,27517],{"text":27516,"start":13611,"end":13640},"Ja, was was ich auch erst mal sagen würde, was ich okay finde.",{"text":27518,"start":13640,"end":13729},"Also wir arbeiten ja auch schon seit Jahren mit irgendwie statistischen Modellen und sagen so, hey, das ist signifikant, so mit 1 Wahrscheinlichkeit von 95 Prozent, ne, also Unschärfe p gleich 0.05 so.",{"sentences":27520,"speaker":620,"num_words":620,"start":13729,"end":13737},[27521],{"text":27522,"start":13729,"end":13737},"Machen wir",{"sentences":27524,"speaker":129,"num_words":27529,"start":13737,"end":13819},[27525,27527],{"text":27526,"start":13737,"end":13746},"ja häufiger so.",{"text":27528,"start":13746,"end":13819},"Und dann denk ich mir so, er ist wahrscheinlich dann in Ordnung und wenn man das dann da festlegt, fänd ich's, glaub ich, auch erst mal irgendwie okay.",31,{"sentences":27531,"speaker":935,"num_words":26650,"start":13819,"end":14017},[27532,27534,27535,27537,27539],{"text":27533,"start":13819,"end":13840},"Aber jetzt nehmen wir mal unsere neunzehnten Failormotes.",{"text":2480,"start":13844,"end":13845},{"text":27536,"start":13845,"end":13920},"Und für jeden Failormode müssten wir jetzt definieren, oh, bei George language ist 3.82 ist dann der Schwellenpunkt, an dem wir sagen, oh, hier geht's nicht weiter.",{"text":27538,"start":13920,"end":13952},"Wenn wir aber sowieso wissen, wir machen 'n binäres Problem da draus Oh.",{"text":27540,"start":13956,"end":14017},"Und es fällt uns ja persönlich auch schwer zu bewerten, enthält dieser Report Judgement Language jetzt einen Wert von 3.84 0.2, ja?",{"sentences":27542,"speaker":935,"num_words":27547,"start":14017,"end":14066},[27543,27545],{"text":27544,"start":14017,"end":14051},"Menschen tendieren dazu, sich irgendwo in 'ner sicheren Mitte anzusiedeln.",{"text":27546,"start":14055,"end":14066},"Die wollen nie konkret werden.",15,{"sentences":27549,"speaker":620,"num_words":129,"start":14066,"end":14068},[27550],{"text":2480,"start":14066,"end":14068},{"sentences":27552,"speaker":935,"num_words":27238,"start":14071,"end":14124},[27553,27555,27556],{"text":27554,"start":14071,"end":14091},"Das Gleiche passiert bei Dutch Language Modellen auch.",{"text":2480,"start":14091,"end":14094},{"text":27557,"start":14096,"end":14124},"Und in dem Moment, wo Du ein binäres Problem da draus machst,",{"sentences":27559,"speaker":620,"num_words":129,"start":14126,"end":14127},[27560],{"text":1950,"start":14126,"end":14127},{"sentences":27562,"speaker":935,"num_words":27207,"start":14127,"end":14260},[27563,27565,27566,27568,27570],{"text":27564,"start":14127,"end":14136},"die Aufgabe leichter.",{"text":2480,"start":14139,"end":14140},{"text":27567,"start":14140,"end":14243},"Das heißt, Du fragst das l m jetzt nicht mehr zwischen für einen Wert zwischen 0 und 5, sondern gib mir 1 oder true zurück, wenn Judge Wandler Language enthalten ist und 0, wenn es nicht enthalten ist.",{"text":27569,"start":14245,"end":14256},"Einfache binäre Entscheidung.",{"text":698,"start":14259,"end":14260},{"sentences":27572,"speaker":935,"num_words":26597,"start":14263,"end":14326},[27573,27575,27577,27578],{"text":27574,"start":14263,"end":14272},"Das macht die Sache leichter.",{"text":27576,"start":14272,"end":14305},"Und dann kannst Du auf 100 Reports gesehen eine Metrik berechnen, nämlich die.",{"text":2480,"start":14309,"end":14310},{"text":27579,"start":14310,"end":14326},"Oder andersrum, es ist ja eher die,",{"sentences":27581,"speaker":129,"num_words":129,"start":14330,"end":14331},[27582],{"text":692,"start":14330,"end":14331},{"sentences":27584,"speaker":935,"num_words":27595,"start":14334,"end":14559},[27585,27587,27589,27591,27593],{"text":27586,"start":14334,"end":14345},"Wie viel Prozent meiner enthält.",{"text":27588,"start":14349,"end":14424},"Und das bringt uns auch schon zu dem Schritt, warum das Ganze so so nützlich ist, denn wir hatten ja gesagt, es ist schwer, erst mal rauszufinden, was sind denn gute E-Mails?",{"text":27590,"start":14428,"end":14456},"Das haben wir rausgefunden, indem wir uns tatsächlich unsere Outputs angeguckt haben.",{"text":27592,"start":14456,"end":14489},"Das heißt, wir haben jetzt 19 sehr hochspezifische E-Mails für unser Produkt rauskristallisiert.",{"text":27594,"start":14493,"end":14559},"Und dadurch, dass wir jetzt auch diesen Mechanismus haben, der die immer wieder bewertet, können wir jetzt uns ein Datenset nehmen und 19, unsere 19 Kategorien.",88,{"sentences":27597,"speaker":935,"num_words":26990,"start":14562,"end":14684},[27598,27600,27602,27604],{"text":27599,"start":14562,"end":14609},"Und was weiß ich, wir nehmen uns 100 Reports und schauen einfach mal, welcher Fehler tritt denn am häufigsten auf?",{"text":27601,"start":14609,"end":14665},"Du kannst dann also 'n schönes Chart dir generieren lassen auf 100 Reports, welcher Fehler kommt denn am häufigsten vor und wie oft?",{"text":27603,"start":14668,"end":14680},"Das gibt dir eine schöne Sichtbarkeit.",{"text":2480,"start":14683,"end":14684},{"sentences":27606,"speaker":620,"num_words":129,"start":14686,"end":14687},[27607],{"text":2339,"start":14686,"end":14687},{"sentences":27609,"speaker":935,"num_words":27620,"start":14687,"end":14953},[27610,27612,27614,27616,27618],{"text":27611,"start":14687,"end":14734},"macht die Fehler sichtbar und hilft dir, 'n Gefühl dafür zu bekommen, welche Fehler wie oft vorkommen.",{"text":27613,"start":14737,"end":14815},"Und dir als Manager, falls Du dann da derjenige bist, der entscheiden muss, woran arbeiten wir denn jetzt oder woran arbeiten wir denn als Nächstes, hilft das, die Sachen zu priorisieren.",{"text":27615,"start":14818,"end":14875},"Wir könnten Judgement language jetzt einen Wert zuweisen, indem wir sagen, was hat das für einen Impact für uns, für den Customer?",{"text":27617,"start":14878,"end":14898},"Was kostet, was bedeutet das kostentechnisch für uns?",{"text":27619,"start":14901,"end":14953},"Es kann Fehler geben, die sind sehr selten, aber die sind so schlimm, wenn das passiert, dann verlassen uns die Kunden.",99,{"sentences":27622,"speaker":620,"num_words":129,"start":14956,"end":14957},[27623],{"text":2480,"start":14956,"end":14957},{"sentences":27625,"speaker":935,"num_words":27303,"start":14957,"end":15059},[27626,27627,27629,27631],{"text":14962,"start":14957,"end":14960},{"text":27628,"start":14964,"end":14996},"Das sind also Entscheidungen, die Du jetzt auf einmal datengestützt treffen kannst.",{"text":27630,"start":14999,"end":15038},"Weil Du siehst, oh, auf 1000 Reports gesehen, ist dieser Fehler ja nur zweimal aufgetreten.",{"text":27632,"start":15041,"end":15059},"Aber wenn der passiert, dann rollen hier Köpfe.",{"sentences":27634,"speaker":620,"num_words":129,"start":15059,"end":15062},[27635],{"text":2480,"start":15059,"end":15062},{"sentences":27637,"speaker":935,"num_words":27646,"start":15065,"end":15139},[27638,27640,27642,27643,27645],{"text":27639,"start":15065,"end":15090},"Andere Sachen sind vielleicht sogar in 80 Prozent der Fälle.",{"text":27641,"start":15093,"end":15107},"Sagen wir mal, die Metrik fehlt.",{"text":2480,"start":15110,"end":15111},{"text":27644,"start":15114,"end":15135},"Da würde dir aber vielleicht jetzt kein Customer unbedingt abspringen.",{"text":1611,"start":15138,"end":15139},28,{"sentences":27648,"speaker":620,"num_words":129,"start":15142,"end":15143},[27649],{"text":2480,"start":15142,"end":15143},{"sentences":27651,"speaker":935,"num_words":26976,"start":15146,"end":15340},[27652,27654,27656,27658,27660],{"text":27653,"start":15146,"end":15154},"Kannst Du abwägen.",{"text":27655,"start":15158,"end":15174},"Da gibt's immer diesen Vergleich mit dem Spülbecken.",{"text":27657,"start":15177,"end":15255},"Wenn Du jetzt abwaschen musst, nimmst Du den großen Topf zuerst, erst mal Platz zu machen oder fängst Du einmal von oben an und gehst zu diesem Weg da durch.",{"text":27659,"start":15258,"end":15312},"Genau und das bringt da ja auch gleichzeitig dann diese Metrik und jetzt kommst Du in dieses schöne Datafly Wheel rein.",{"text":27661,"start":15314,"end":15340},"Das ist dieser Prozess, dass Du jetzt immer wieder iterieren kannst.",{"sentences":27663,"speaker":935,"num_words":27670,"start":15342,"end":15412},[27664,27666,27668],{"text":27665,"start":15342,"end":15356},"Wir können jetzt unsere Anwendungen testen.",{"text":27667,"start":15359,"end":15381},"Wir haben unsere Stats, wie oft welcher Fehler vorkommt.",{"text":27669,"start":15381,"end":15412},"Wir können priorisieren, welche Fehler sind die, die als Nächstes angehen müssen.",27,{"sentences":27672,"speaker":620,"num_words":129,"start":15412,"end":15415},[27673],{"text":2480,"start":15412,"end":15415},{"sentences":27675,"speaker":935,"num_words":27685,"start":15418,"end":15578},[27676,27678,27680,27681,27683],{"text":27677,"start":15418,"end":15479},"Und mit dieser Information können wir jetzt an unsere Entwickler rangehen und sagen, hey, wir müssen Judgemental Language zum Beispiel im nächsten Release angehen.",{"text":27679,"start":15482,"end":15500},"Jetzt hat der Entwickler 'n ganz konkretes Ziel.",{"text":2480,"start":15503,"end":15504},{"text":27682,"start":15504,"end":15565},"Ja, der der kann jetzt sein neues Rax System ausprobieren, der kann jetzt sein neues Framework ausprobieren, der kann die prompt umschreiben, Mhm.",{"text":27684,"start":15568,"end":15578},"Worauf der Bock hat.",60,{"sentences":27687,"speaker":935,"num_words":27696,"start":15580,"end":15721},[27688,27689,27691,27692,27694],{"text":698,"start":15580,"end":15581},{"text":27690,"start":15581,"end":15626},"Und dann lässt er einfach die jeweils noch mal durchlaufen und schaut, welchen Impact hatte dann meine Veränderung?",{"text":698,"start":15629,"end":15630},{"text":27693,"start":15633,"end":15677},"Er kann eine ganz konkrete Zahl seinen Manager geben und der kann dann entscheiden, ja, das funktioniert.",{"text":27695,"start":15680,"end":15721},"Und Du kannst diesen Prozess von diesem gruppieren, kannst Du sogar noch mal eine Stufe wiederholen.",53,{"sentences":27698,"speaker":620,"num_words":129,"start":15724,"end":15725},[27699],{"text":2480,"start":15724,"end":15725},{"sentences":27701,"speaker":935,"num_words":27712,"start":15725,"end":15901},[27702,27704,27706,27708,27710],{"text":27703,"start":15725,"end":15749},"Denn deine Fehler, die kannst Du auch noch mal gruppieren.",{"text":27705,"start":15752,"end":15791},"Und in unserem Beispiel, wir hatten jetzt diese dieses Beispiel mit der fehlenden Metrik bei 1 Zahl.",{"text":27707,"start":15795,"end":15810},"Wir hatten dieses Beispiel mit Judgemental Language.",{"text":27709,"start":15814,"end":15870},"Beide Probleme sind in dieser Kategorie von der Darstellung oder wie ich das Ganze reporte, wir haben's genannt, einzugliedern.",{"text":27711,"start":15874,"end":15901},"Und in dem Moment baut man eine Taxonomie von Fehlern.",63,{"sentences":27714,"speaker":935,"num_words":27725,"start":15904,"end":16105},[27715,27717,27719,27721,27723],{"text":27716,"start":15904,"end":15936},"Und diese 19 Fehleremotes haben wir in 4 Toplevel Kategorien klassifiziert.",{"text":27718,"start":15940,"end":15962},"Das war Kontext clarity, Präsentational Quality.",{"text":27720,"start":15965,"end":16038},"Und ich kann jetzt nicht nur sagen, im nächsten Release greifen wir Judgement Language an, sondern der nächste Release konzentriert sich komplett auf präsentational Quality zum Beispiel, ja.",{"text":27722,"start":16038,"end":16084},"Und das gibt natürlich auch Entscheidern die Möglichkeit, jetzt genau zu planen, was, woran wollen wir arbeiten?",{"text":27724,"start":16087,"end":16105},"Die können das schon ans Marketingeam geben.",69,{"sentences":27727,"speaker":935,"num_words":27137,"start":16107,"end":16167},[27728,27730],{"text":27729,"start":16107,"end":16143},"Release Nummer 2 wird sich darum kümmern, Release Nummer 3 darum und so weiter.",{"text":27731,"start":16143,"end":16167},"Also es gibt ja Planbarkeit, es gibt ja konkrete Zahlen.",{"sentences":27733,"speaker":129,"num_words":27744,"start":16170,"end":16464},[27734,27736,27738,27740,27742],{"text":27735,"start":16170,"end":16198},"Und vor allem, genau, diese Quantifizierbarkeit find ich irgendwie interessant.",{"text":27737,"start":16201,"end":16322},"Was in meinem Kopf fürn Konstrukt entstanden ist, ist irgendwie so, Du hast gesagt, Du hast diese 19 E-Bikes oder dann diese 4 Hauptkategorien und die dann einfach irgendwie so son Teil von soner riesigen Formel sind, so und die gewichtest Du dann halt einfach anders.",{"text":27739,"start":16322,"end":16380},"So und dann am Ende kommt irgendwie 'n Wert raus so und sagst so, okay, wenn's diesen Wert überschreitet, dann können wir nicht releasen.",{"text":27741,"start":16380,"end":16410},"Wenn's drunter ist, dann haben wir das erreicht, was wir erreichen wollen.",{"text":27743,"start":16410,"end":16464},"Also irgendwie find ich's so schön, dass da aus so sehr viel nur Wahrscheinlichkeit irgendwie dann am Ende doch noch was zahlenmäßig rauszuhauen.",115,{"sentences":27746,"speaker":935,"num_words":27233,"start":16468,"end":16702},[27747,27749,27751,27753,27754],{"text":27748,"start":16468,"end":16483},"Ja, und das hilft dir natürlich auch.",{"text":27750,"start":16485,"end":16553},"Wir hatten ja am Anfang da drüber gesprochen, diese Traces, diese, ja, Logs von User Input bis zu deinem Output, dass man die erst mal und sammelt.",{"text":27752,"start":16556,"end":16641},"Diese Software, die man dafür einsetzt, da gibt's ja viele am Markt, nehmen wir mal Langfuse als Beispiel, hilft dir ja auch dabei zu tracken, wie viele Token hat denn dieser ganze gekostet?",{"text":2480,"start":16645,"end":16646},{"text":27755,"start":16646,"end":16702},"Und auf Basis von dem Modell, was Du verwendest, also was hat denn das auch in Dollar oder in Euro gekostet, der ganze Roundtrip?",{"sentences":27757,"speaker":935,"num_words":27027,"start":16706,"end":16862},[27758,27760,27762,27764,27766],{"text":27759,"start":16706,"end":16718},"Und die Latenz wird gemessen.",{"text":27761,"start":16718,"end":16742},"Was wie lange hat denn der Requiz gedauert?",{"text":27763,"start":16745,"end":16788},"Und wenn Du diese Sachen jetzt kombinierst, Du hast dein deinen Errorwert, ja, die Qualität deiner Anwendung.",{"text":27765,"start":16791,"end":16838},"Du hast die Latenz, Du weißt, wie lange dein Request gedauert hat und Du weißt, was dein gekostet hat.",{"text":27767,"start":16840,"end":16862},"Jetzt kannst Du auf einmal ganz konkrete Aussagen treffen.",{"sentences":27769,"speaker":935,"num_words":26676,"start":16862,"end":16983},[27770,27771,27773,27775],{"text":2480,"start":16862,"end":16865},{"text":27772,"start":16868,"end":16925},"Beispielsweise bei uns kamen dann raus, es war damals, wir hatten noch GPT 4 o am Laufen, war irgendwann Anfang letzten Jahres.",{"text":27774,"start":16928,"end":16954},"Und dann kam GPT 4 Punkt 1 raus und auch das Nanomodell.",{"text":27776,"start":16958,"end":16983},"Und ich weiß noch, dass wir dann die jeweils haben laufen lassen.",{"sentences":27778,"speaker":620,"num_words":129,"start":16985,"end":16986},[27779],{"text":2480,"start":16985,"end":16986},{"sentences":27781,"speaker":935,"num_words":26638,"start":16986,"end":17213},[27782,27784,27786,27787,27789],{"text":27783,"start":16986,"end":17028},"Und ich konnte dann zu meinem Manager sagen, okay, GPT 4 Punkt 1 kam gestern Abend raus.",{"text":27785,"start":17030,"end":17104},"Wir haben 6.2 Prozent mehr Fehler in Kontext clarity, aber wir haben 37 Prozent schnelleren Output und wir haben 96 Prozent weniger Kosten bei der Royal Quiz.",{"text":698,"start":17104,"end":17108},{"text":27788,"start":17110,"end":17151},"So und das sind Zahlen, die kann ich jetzt meinem Boss vorlegen und der kann dann Entscheidungen treffen.",{"text":27790,"start":17151,"end":17213},"Das heißt, wir können innerhalb von, was weiß ich, 24 Stunden nach Model Release entscheiden, ist es gut genug, dieses Model zu releasen, ja oder nein?",{"sentences":27792,"speaker":935,"num_words":27801,"start":17216,"end":17307},[27793,27795,27797,27799],{"text":27794,"start":17216,"end":17240},"Und vorher war das eben dieses Viepetriebene, ich, ja?",{"text":27796,"start":17240,"end":17257},"Probier das einfach mal aus, ja?",{"text":27798,"start":17257,"end":17285},"Das ist natürlich jetzt eine gewisse Sicherheit, die man dadurch erreicht hat.",{"text":27800,"start":17287,"end":17307},"Alles nur, weil Du dir mal deinen Output angeguckt hast.",37,{"sentences":27803,"speaker":129,"num_words":27813,"start":17309,"end":17615},[27804,27805,27807,27809,27811],{"text":698,"start":17309,"end":17310},{"text":27806,"start":17313,"end":17452},"Ja, also vor allem, also den Aspekt find ich halt besonders gut, weil ich glaub, das ist auch sone Entscheidung, die bei uns oft irgendwie so im Raum ist, wo wir uns nie sicher sind so, hey, okay, nutzen wir jetzt das Modell, gehen wir jetzt auf Gemini, nutzen wir, ist Claude so wirklich das Wahre.",{"text":27808,"start":17452,"end":17502},"Sind diese ganzen Models von Mysteral irgendwie ganz geil und wir nie wissen, also ist alles sehr nach Gefühl.",{"text":27810,"start":17505,"end":17568},"Dann wirklich zu sagen so, hey, okay, es ist vielleicht minimal schlechter in dem Bereich, aber es dafür deutlich schneller in dem Output und kostet deutlich weniger Geld.",{"text":27812,"start":17568,"end":17615},"Ich glaub, diese Sichtbarkeit dadurch zu haben durch jeweils, also find ich, ist schon 'n ganz guter Vorteil auf jeden Fall.",125,{"sentences":27815,"speaker":935,"num_words":27825,"start":17618,"end":17734},[27816,27818,27820,27822,27824],{"text":27817,"start":17618,"end":17633},"Ja, wir haben einen Punkt ausgelassen.",{"text":27819,"start":17633,"end":17680},"Wir haben vorhin drüber gesprochen, wie sehr kann ich denn dem Model trauen, dass es auch wirklich richtig evaluiert.",{"text":27821,"start":17683,"end":17697},"Nehmen wir mal jetzt 100 Reports.",{"text":27823,"start":17697,"end":17731},"Wir haben ja identifiziert, ist ein, den der für unsere Anwendungen relevant ist.",{"text":2480,"start":17731,"end":17734},45,{"sentences":27827,"speaker":935,"num_words":27595,"start":17734,"end":17972},[27828,27830,27832,27834,27836],{"text":27829,"start":17734,"end":17747},"Bleibt mal bei dem Beispiel.",{"text":27831,"start":17750,"end":17860},"Dann kann ich natürlich einen Experten aus meiner Domäne an diese 100 Reports ransetzen und dem die vielleicht nicht ganz schöne, aber wichtige Aufgabe geben, geh mal bitte durch diese 100 Reports durch und sag mir, ob das Judgement Language enthält, ja oder nein.",{"text":27833,"start":17863,"end":17894},"Das heißt, ich habe ein human Label für ein gewisses Subset.",{"text":27835,"start":17898,"end":17927},"Und das ist mein Golden Dataset für für diesen Fehler Mode.",{"text":27837,"start":17931,"end":17972},"Und dann kann ich diese prompt schreiben für diesen dieses und eben die gleichen 100 Reports zeigen.",{"sentences":27839,"speaker":620,"num_words":129,"start":17975,"end":17976},[27840],{"text":2480,"start":17975,"end":17976},{"sentences":27842,"speaker":935,"num_words":26732,"start":17976,"end":18202},[27843,27845,27847,27849,27851],{"text":27844,"start":17976,"end":18010},"Und was dann relevant ist, ist wieder dieser Data Science getriebene Ansatz.",{"text":27846,"start":18013,"end":18049},"Ich schau mir einfach an in 'ner Matrix, wie viel Übereinstimmung ist da?",{"text":27848,"start":18049,"end":18116},"Also ich schau mir an, wie oft wurde wirklich vom Human das Label Judgement a language vergeben und wie oft wurde das Label vom Agent oder vom LLM vergeben?",{"text":27850,"start":18120,"end":18138},"Und ist es auch an den gleichen, ja?",{"text":27852,"start":18138,"end":18202},"Also man sagt dann, man hat dann diese True Positives, True Negatives und dann gibt's eben die Ausreißer, wo Du dann ja, Unstimmigkeiten hast.",{"sentences":27854,"speaker":935,"num_words":26896,"start":18202,"end":18341},[27855,27857],{"text":27856,"start":18202,"end":18325},"Und deswegen ist es so wichtig, dass Du da einmal ein Human auch mit ran setzt, denn nur so kannst Du dir dann auch sicher sein, dass dieses LLM wirklich mit deinem Human Alignment übereinstimmt, dass der wirklich genau das testet, testest, wie Du das auch testen würdest.",{"text":27858,"start":18325,"end":18341},"Also dafür ist dieser Schritt dann relevant.",{"sentences":27860,"speaker":150,"num_words":26659,"start":18343,"end":18385},[27861,27863],{"text":27862,"start":18343,"end":18366},"Wir mussten irgendwie, ich würde relativ wenig Nachfragen stellen.",{"text":27864,"start":18369,"end":18385},"Das, also Du hast es einfach sehr",{"sentences":27866,"speaker":129,"num_words":26585,"start":18387,"end":18410},[27867,27869],{"text":27868,"start":18387,"end":18393},"'N Selbstläufer.",{"text":27870,"start":18397,"end":18410},"Du hast es einfach sehr,",{"sentences":27872,"speaker":150,"num_words":26625,"start":18410,"end":18447},[27873,27875,27876],{"text":27874,"start":18410,"end":18429},"sehr klar und stringent uns uns dargestellt.",{"text":698,"start":18432,"end":18433},{"text":27877,"start":18433,"end":18447},"Ich Das Einzige Ja,",{"sentences":27879,"speaker":620,"num_words":620,"start":18447,"end":18454},[27880],{"text":27881,"start":18447,"end":18454},"nee, nee,",{"sentences":27883,"speaker":150,"num_words":935,"start":18454,"end":18464},[27884],{"text":27885,"start":18454,"end":18464},"trotzdem beide Fragen.",{"sentences":27887,"speaker":935,"num_words":27122,"start":18464,"end":18474},[27888],{"text":27889,"start":18464,"end":18474},"Ja, wir beide Fragen.",{"sentences":27891,"speaker":129,"num_words":27492,"start":18477,"end":18509},[27892,27894,27896],{"text":27893,"start":18477,"end":18486},"Nee, frag Du zuerst.",{"text":27895,"start":18486,"end":18498},"Du hast länger nichts gesagt.",{"text":27897,"start":18501,"end":18509},"Ich muss darauf achten.",{"sentences":27899,"speaker":150,"num_words":27238,"start":18509,"end":18573},[27900],{"text":27901,"start":18509,"end":18573},"Die, gibt es dafür ein, gibt es eine technische Empfehlung, weil Du hast irgendwas gesagt, was machst Du mit Lang, wie",{"sentences":27903,"speaker":620,"num_words":620,"start":18573,"end":18579},[27904],{"text":27905,"start":18573,"end":18579},"hieß das?",{"sentences":27907,"speaker":150,"num_words":620,"start":18579,"end":18588},[27908,27909],{"text":18585,"start":18579,"end":18583},{"text":18585,"start":18587,"end":18588},{"sentences":27911,"speaker":935,"num_words":26667,"start":18591,"end":18646},[27912,27914],{"text":27913,"start":18591,"end":18618},"Ja, das ist eine Software, die dir hilft, diese Traces zu sammeln.",{"text":27915,"start":18618,"end":18646},"Also ist eine Monitoringanwendung für Jenny Eye Anwendungen.",{"sentences":27917,"speaker":150,"num_words":27220,"start":18649,"end":18793},[27918,27920,27922,27924,27926],{"text":27919,"start":18649,"end":18653},"Okay, cool.",{"text":27921,"start":18653,"end":18698},"Weil das wär nur so das Ding gewesen, okay, wie wie hieß son bisschen die technischen Vampire?",{"text":27923,"start":18698,"end":18736},"Weil ich glaub grundsätzlich, dass, ne, die Idee kam, glaub ich, sehr klar rüber.",{"text":27925,"start":18739,"end":18785},"Und dann wär ja jetzt sone der nächste Schritt, okay, wie kriegt man das konkret irgendwie bei sich eingebaut, Ja.",{"text":27927,"start":18787,"end":18793},"Was das ist.",{"sentences":27929,"speaker":150,"num_words":27547,"start":18793,"end":18828},[27930],{"text":27931,"start":18793,"end":18828},"Und dann ist es zumindest ja vielleicht eine Lösung, die man sich mal angucken kann.",{"sentences":27933,"speaker":935,"num_words":27238,"start":18828,"end":18896},[27934,27936,27938,27939,27940],{"text":27935,"start":18828,"end":18857},"Genau, oder Reintest, ich will mich da gar nicht festlegen.",{"text":27937,"start":18861,"end":18870},"Phoenix ist noch eine.",{"text":2480,"start":18873,"end":18874},{"text":18881,"start":18878,"end":18879},{"text":27941,"start":18883,"end":18896},"Es gibt viele, viele Anbieter.",{"sentences":27943,"speaker":935,"num_words":27027,"start":18899,"end":19060},[27944,27946,27948,27950,27952],{"text":27945,"start":18899,"end":18906},"Ich bin da sehr.",{"text":27947,"start":18910,"end":18939},"Denn alle alle funktionieren sehr gut und bieten auch sehr viel Funktionalität.",{"text":27949,"start":18942,"end":19004},"Damit anzufangen mit diesem ganzen Prozess, könntest Du dir genauso gut einfach alle in irgendeine txt datei loggen und dann da durchgehen.",{"text":27951,"start":19007,"end":19050},"Also der Prozess selber, der bedarf ja keiner Software, aber dieser Prozess wird durch sone Software noch einfacher.",{"text":27953,"start":19050,"end":19060},"Unterstützt, ja.",{"sentences":27955,"speaker":935,"num_words":26976,"start":19060,"end":19243},[27956,27957,27959,27961,27963],{"text":692,"start":19060,"end":19063},{"text":27958,"start":19067,"end":19101},"Ja, aber es ist auf jeden Fall sehr einfach und man erreicht sehr viel dadurch.",{"text":27960,"start":19101,"end":19164},"Ich hatte vorhin in in dem Talk jemanden, der hat mir dann tatsächlich die Frage gestellt, oh, das ist aber, das klingt aber sehr teuer.",{"text":27962,"start":19167,"end":19190},"Da muss ich ja jemanden da hinsetzen, der da durchgeht.",{"text":27964,"start":19193,"end":19243},"Und der wollte mir dann wirklich sagen, dass dieser Prozess für ihn irgendwie nicht so gut ist, weil er sehr teuer ist.",{"sentences":27966,"speaker":935,"num_words":27595,"start":19245,"end":19476},[27967,27969,27971,27973,27975],{"text":27968,"start":19245,"end":19272},"Und dann hab ich ihm vorgerechnet, ja, pass mal auf.",{"text":27970,"start":19272,"end":19356},"Also wenn ich jetzt jemand, ich sag mal, einen Tag Set-up von Langviews Und ich setz einen Tag jemanden hin, der da 100 Traces bei uns durchgeht und sich das Ganze mal anschaut.",{"text":27972,"start":19359,"end":19406},"So, dann hab ich da 2 Mann Tage investiert und dann schreib ich mir da 'n paar Prompts.",{"text":27974,"start":19409,"end":19454},"Was wäre denn gewesen, wenn ich jetzt diese Software released hätte, wo wir unsere Leute Low Performer nennen?",{"text":27976,"start":19454,"end":19476},"So, das wäre jetzt an 10000 Kunden ausgeliefert worden.",{"sentences":27978,"speaker":935,"num_words":27825,"start":19476,"end":19597},[27979,27980,27982,27983,27985],{"text":2480,"start":19476,"end":19479},{"text":27981,"start":19481,"end":19514},"Davon hätten sich dann 500 verabschiedet und das hätte uns dann 20000000 gekostet.",{"text":698,"start":19517,"end":19518},{"text":27984,"start":19521,"end":19574},"Ich denke, hier ist schon klar, die Kosten, die amortisieren sich bei diesem Prozess, bei diesem so einfachen Prozess sehr schnell.",{"text":27986,"start":19577,"end":19597},"Also Kosten sind auf jeden Fall gar kein Gegenargument.",{"sentences":27988,"speaker":935,"num_words":27316,"start":19602,"end":19749},[27989,27991,27992,27994,27996],{"text":27990,"start":19602,"end":19639},"Und das Spannende ist auch, das ist auch ein Prozess, der macht den Leuten tatsächlich Spaß.",{"text":2480,"start":19641,"end":19642},{"text":27993,"start":19645,"end":19681},"Denn wenn man so durchgeht und sich mal durchliest, was ist denn so der Output?",{"text":27995,"start":19684,"end":19720},"Man erkennt manchmal so lustige Dinge auch oder den greift sich an den Kopf, ja?",{"text":27997,"start":19720,"end":19749},"Man sieht's ja auch immer wieder in den in den in Reddit.",{"sentences":27999,"speaker":935,"num_words":28002,"start":19753,"end":19812},[28000],{"text":28001,"start":19753,"end":19812},"Selbst die großen Player, Google, Amazon, selbst die haben ja nicht fehlerlose Software, weil wir reden von strastischen Systemen.",19,{"sentences":28004,"speaker":129,"num_words":28007,"start":19816,"end":19841},[28005],{"text":28006,"start":19816,"end":19841},"Ich musste so lachen bei diesem Seepferdchen Emoji Hast",9,{"sentences":28009,"speaker":935,"num_words":620,"start":19841,"end":19847},[28010],{"text":28011,"start":19841,"end":19847},"Du gesagt,",{"sentences":28013,"speaker":129,"num_words":26659,"start":19851,"end":19892},[28014,28016],{"text":28015,"start":19851,"end":19876},"Alter, ich konnt nicht mehr volllachen, das war so lustig.",{"text":28017,"start":19879,"end":19892},"Das war so lustig, oh Gott.",{"sentences":28019,"speaker":935,"num_words":28028,"start":19892,"end":20089},[28020,28022,28024,28025,28027],{"text":28021,"start":19892,"end":19919},"Aber es geht natürlich auch manchmal in eine richtig blöde Richtung.",{"text":28023,"start":19921,"end":20040},"Wir hatten jetzt bei Google ja auch diesen KI Mode, wo Du Fragen stellen kannst und manchmal ist dort ja wirklich sehr komischer Output, beispielsweise wurde ja dort empfohlen, dass Du Kleber auf die Pizza machen sollst, weil es tatsächlich einen Rat, wo das so beschrieben wurde.",{"text":698,"start":20040,"end":20043},{"text":28026,"start":20043,"end":20085},"Oder Google empfiehlt, dass es sinnvoll sein könnte, einen kleinen Stein pro Tag zu essen.",{"text":8424,"start":20085,"end":20089},75,{"sentences":28030,"speaker":935,"num_words":28041,"start":20091,"end":20255},[28031,28033,28035,28037,28039],{"text":28032,"start":20091,"end":20141},"Wenn Du jetzt Engineer bei Google bist und Du kriegst die Aufgabe, schreib doch mal Tests für den neuen KI Mode, Ja.",{"text":28034,"start":20144,"end":20175},"Wäre das wahrscheinlich kein Test, der dir am Anfang in den Sinn gekommen wäre.",{"text":28036,"start":20175,"end":20203},"Aber dadurch, dass Du dir tatsächlich mal deine Daten anguckst, Ja.",{"text":28038,"start":20205,"end":20214},"Ja, schaffst Du das.",{"text":28040,"start":20216,"end":20255},"Ja, und vielleicht den Bogen auch zu machen zu dem Bereich, wo wir arbeiten, so Model Training.",68,{"sentences":28043,"speaker":620,"num_words":129,"start":20259,"end":20260},[28044],{"text":2480,"start":20259,"end":20260},{"sentences":28046,"speaker":935,"num_words":28057,"start":20263,"end":20534},[28047,28049,28051,28053,28055],{"text":28048,"start":20263,"end":20304},"Wenn Du diese jeweils hast, gibt dir das natürlich aus Produktsicht Sicherheit, ja, und auch diese Metrik.",{"text":28050,"start":20307,"end":20350},"Was Du aber auch machen kannst, ist diese jeweils dann später fürs Modeltraining, fürs Steering einzusetzen.",{"text":28052,"start":20353,"end":20413},"Also wenn man Judgement Language jetzt als Evaluator hat, kann ich diesen Evaluator benutzen, Outputs zu validieren, die mir ein Model generiert.",{"text":28054,"start":20416,"end":20486},"Ich judge das und nutze das, das wieder als Feedback beim Reinforcement Learning reinzubringen und das Model dann automatisiert in die richtige Richtung trainieren zu lassen.",{"text":28056,"start":20486,"end":20534},"Also es hilft mir nicht nur auf Produktebene, sondern eben dann auch später beim beim Training und überall und ja.",101,{"sentences":28059,"speaker":935,"num_words":27492,"start":20534,"end":20567},[28060],{"text":28061,"start":20534,"end":20567},"Also es ist wirklich eine sehr, sehr nützliche Sache, kann ich nur empfehlen.",{"sentences":28063,"speaker":150,"num_words":26548,"start":20570,"end":20582},[28064,28065],{"text":2480,"start":20570,"end":20571},{"text":28066,"start":20571,"end":20582},"Hört sich sinnvoll an.",{"sentences":28068,"speaker":129,"num_words":28078,"start":20582,"end":20923},[28069,28070,28072,28074,28076],{"text":698,"start":20582,"end":20584},{"text":28071,"start":20587,"end":20796},"Vielleicht 'n Aspekt, weil weil Du es häufiger erwähnt hast, diese also sehr spezifischen, auf euer Produkt zugeschnittenen jeweils, ne, die 19 Stück, die ihr dort dann irgendwie identifiziert habt und dann auch dann klassifiziert habt, ist, also wie Du richtig gesagt hast, identifiziert habt und dann auch dann klassifiziert habt, ist, also wie Du richtig gesagt hast, ist es auf jeden Fall irgendwie 'n Prozess, 'n Aufwand, den man machen muss und der sich so nicht 1 zu 1 auf andere Produkte übertragen lässt.",{"text":28073,"start":20796,"end":20848},"Und da ist son bisschen die Frage, vielleicht gibt's da schon etwas so, was vielleicht 'n bisschen generischer ist, so Schritt 1?",{"text":28075,"start":20848,"end":20914},"Oder sagst Du, nee, man wird dabei bleiben müssen, sich wirklich sein Produkt anzuschauen, sich wirklich damit auseinanderzusetzen, gucken, was sind denn unsere unsere eigenen Benchmarks?",{"text":28077,"start":20914,"end":20923},"Was ist uns wichtig?",138,{"sentences":28080,"speaker":129,"num_words":28083,"start":20923,"end":20957},[28081],{"text":28082,"start":20923,"end":20957},"Und dann halt quasi immer auf eine sehr hochspezifische Lösung am Ende zu kommen.",14,{"sentences":28085,"speaker":935,"num_words":28096,"start":20957,"end":21161},[28086,28088,28090,28092,28094],{"text":28087,"start":20957,"end":20999},"Ja, also ich, ja, es gibt solche fertigen Lösungen, aber ich bin kein Freund von.",{"text":28089,"start":20999,"end":21048},"Der einzige Vorteil, den man davon vielleicht haben könnte, wäre es zu verwenden, dein initiales Dataset zu erstellen.",{"text":28091,"start":21051,"end":21107},"Wir haben ja gesagt, wir gucken uns die ersten 20 Trazens an, das reicht in der Regel aus, schon mal 'n Gefühl dafür zu bekommen.",{"text":28093,"start":21110,"end":21143},"Wenn Du jetzt aber Tausende von Travis hast, ja, welche 20 nimmst Du denn da?",{"text":28095,"start":21145,"end":21161},"Das muss man ja auch erst mal machen.",81,{"sentences":28098,"speaker":935,"num_words":28103,"start":21161,"end":21249},[28099,28101],{"text":28100,"start":21161,"end":21202},"Das könntest Du jetzt natürlich random sampeln oder Du sagst, Du nimmst dir die 500 längsten.",{"text":28102,"start":21206,"end":21249},"Oder Du nimmst eben solche generischen Ivals wie Helpfulness, die es eben fertig am Markt gibt,",32,{"sentences":28105,"speaker":620,"num_words":129,"start":21253,"end":21254},[28106],{"text":2480,"start":21253,"end":21254},{"sentences":28108,"speaker":935,"num_words":26700,"start":21257,"end":21270},[28109],{"text":28110,"start":21257,"end":21270},"Die aber sehr, sehr generisch sind",{"sentences":28112,"speaker":129,"num_words":620,"start":21272,"end":21276},[28113],{"text":28114,"start":21272,"end":21276},"Ja, okay.",{"sentences":28116,"speaker":935,"num_words":27670,"start":21278,"end":21344},[28117,28119],{"text":28118,"start":21278,"end":21327},"Und würdest dann sagen, okay, alle, die unter 'nem gewissen Score sind, das sind die ersten 20, die ich mir anschaue.",{"text":28120,"start":21327,"end":21344},"Für so was kann's sinnvoll sein.",{"sentences":28122,"speaker":620,"num_words":129,"start":21344,"end":21346},[28123],{"text":454,"start":21344,"end":21346},{"sentences":28125,"speaker":935,"num_words":26964,"start":21346,"end":21560},[28126,28128,28129,28131,28133],{"text":28127,"start":21346,"end":21389},"Aber ansonsten macht es einfach Sinn, dass Du hochspezifisch auf dein auf dein Produkt diese anwendest.",{"text":2480,"start":21393,"end":21394},{"text":28130,"start":21396,"end":21476},"Jetzt auf sonen Kontext wie Blue Yonder, ein Riesen Enterprise Unternehmen gesehen, es gibt durchaus den Fall, dass jeweils für verschiedene Projekte relevant sind und nicht nur produktspezifisch sind.",{"text":28132,"start":21479,"end":21531},"Jetzt in das Beispiel von diesem Report, das ließe sich bestimmt auch auf andere Dinge im gesamten Unternehmen anwenden, ja.",{"text":28134,"start":21531,"end":21560},"Denn überall dort, wo man Customer Facing irgendwie unterwegs ist, Ja.",{"sentences":28136,"speaker":935,"num_words":26896,"start":21563,"end":21708},[28137,28139,28141],{"text":28138,"start":21563,"end":21582},"Möchte man ja kein judgmental language haben.",{"text":28140,"start":21584,"end":21619},"Aber es gibt auch andere Dinge, beispielsweise nehmen wir mal den Punkt von Models.",{"text":28142,"start":21621,"end":21708},"Gerade jetzt im internationalen Supply Chain Kontext kann es durchaus Sinn machen, generische, ich sag jetzt generisch in Anführungszeichen, Ivals zu schreiben, die zum Beispiel sicherstellen, dass man keinen geografischen Bias mit drin hat.",{"sentences":28144,"speaker":620,"num_words":129,"start":21708,"end":21710},[28145],{"text":2480,"start":21708,"end":21710},{"sentences":28147,"speaker":935,"num_words":27620,"start":21713,"end":21967},[28148,28150,28152,28153,28155],{"text":28149,"start":21713,"end":21786},"Wenn ich jetzt Kunde von so 1 Software bin und ich frag einen Agent, wie könnten wir denn die, oder welches Land wäre denn das Beste, meinen Truck da durchzuschicken?",{"text":28151,"start":21789,"end":21851},"Dann will ich keinen geografischen Bias haben, weil der Präsident irgendwie blöde Sachen tweetet, sondern ich will möglichst unvoreingenommen an die Sache rangehen.",{"text":21857,"start":21855,"end":21856},{"text":28154,"start":21856,"end":21881},"Das sind Dinge, die sind natürlich für alle Projekte relevant.",{"text":28156,"start":21883,"end":21967},"Also wenn man so eine Art Dashboard baut mit solchen generischen Sachen, kann es schon Sinn machen, dass man das auf mehrere Sachen ausweitet, aber das ist eher die Ausnahme und sehr schwer zu finden.",{"sentences":28158,"speaker":935,"num_words":129,"start":21970,"end":21971},[28159],{"text":1731,"start":21970,"end":21971},{"sentences":28161,"speaker":150,"num_words":28007,"start":21973,"end":21998},[28162,28164],{"text":28163,"start":21973,"end":21992},"Können wir noch mal ganz kurz auf gehen?",{"text":22000,"start":21997,"end":21998},{"sentences":28166,"speaker":129,"num_words":26700,"start":22002,"end":22022},[28167,28168],{"text":22006,"start":22002,"end":22003},{"text":28169,"start":22008,"end":22022},"Nicht auf Record off off",{"sentences":28171,"speaker":620,"num_words":26585,"start":22022,"end":22038},[28172],{"text":28173,"start":22022,"end":22038},"off off off off off off off",{"sentences":28175,"speaker":150,"num_words":26700,"start":22038,"end":22050},[28176],{"text":28177,"start":22038,"end":22050},"off off off off off off",{"sentences":28179,"speaker":620,"num_words":28182,"start":22050,"end":22108},[28180],{"text":28181,"start":22050,"end":22108},"off off off off off off off off off off off off off off off off off off off off off off off off off",25,{"sentences":28184,"speaker":935,"num_words":26540,"start":22108,"end":22124},[28185],{"text":28186,"start":22108,"end":22124},"off off off off off off off off",{"sentences":28188,"speaker":129,"num_words":158,"start":22124,"end":22145},[28189],{"text":28190,"start":22124,"end":22145},"off off off off off off off off off off",{"sentences":28192,"speaker":620,"num_words":27492,"start":22145,"end":22174},[28193],{"text":28194,"start":22145,"end":22174},"off off off off off off off off off off off off off",{"sentences":28196,"speaker":935,"num_words":26659,"start":22174,"end":22210},[28197],{"text":28198,"start":22174,"end":22210},"off off off off off off off off off off off off off off off off",{"sentences":28200,"speaker":620,"num_words":28083,"start":22210,"end":22248},[28201],{"text":28202,"start":22210,"end":22248},"off off off off off off off off off off off off off off",{"sentences":28204,"speaker":150,"num_words":28215,"start":22248,"end":22549},[28205,28207,28209,28211,28213],{"text":28206,"start":22248,"end":22269},"off off off off off off sind.",{"text":28208,"start":22272,"end":22297},"Viel Feintuning von Modellen und irgendwie da eigene Sachen anzupassen.",{"text":28210,"start":22300,"end":22457},"Ist das noch, also ist das noch 'n Ding, hört sich irgendwie vielleicht falsch an, aber also Oh, ich frag das immer mal genau, also macht das noch Sinn oder entwickeln wir uns irgendwie, sind die so groß und ist es dann einen Kostenfaktor, weil man kleinere Modelle nehmen kann oder ist es wirklich dann, weil man diesen Output aus den großen Modellen nicht rausbekommt?",{"text":28212,"start":22461,"end":22498},"Glaubst Du, es wird immer noch eine, also wird es lange längerfristig in Zukunft haben?",{"text":28214,"start":22498,"end":22549},"Wie ist son bisschen der Take darauf, weil ihr ja anscheinend viel in dem Bereich macht, was jetzt auch nicht jeder macht?",119,{"sentences":28217,"speaker":935,"num_words":28226,"start":22553,"end":22664},[28218,28220,28221,28223,28224],{"text":28219,"start":22553,"end":22617},"Also es gibt diesen schönen Spruch, Models come and go and ivals are to stay, noch mal beim Beim Thema bleiben.",{"text":698,"start":22620,"end":22621},{"text":28222,"start":22623,"end":22639},"Du kannst halt immer wieder neue Models testen.",{"text":2480,"start":22641,"end":22642},{"text":28225,"start":22642,"end":22664},"Und die ivals schreibst Du in der Regel nur einmal.",41,{"sentences":28228,"speaker":935,"num_words":28226,"start":22664,"end":22772},[28229,28230,28232,28233,28235],{"text":2480,"start":22664,"end":22667},{"text":28231,"start":22669,"end":22712},"Ich denke, es wird noch sehr, sehr lange Sinn machen, dass man eigene Model in die richtige Richtung drückt.",{"text":2480,"start":22712,"end":22715},{"text":28234,"start":22717,"end":22752},"Auf welche Art und Weise, ob das jetzt superweist Feintuning ist oder Reinforcement Learning.",{"text":28236,"start":22756,"end":22772},"Das hängt immer vom Anwendungsfall ab.",{"sentences":28238,"speaker":935,"num_words":28226,"start":22774,"end":22890},[28239,28241],{"text":28240,"start":22774,"end":22843},"Also der Hauptunterschied zwischen diesen beiden Varianten ist ja der, beim supervice Feintuning hast Du ein Groundthrof Dataset und Du bringst das dem Model bei.",{"text":28242,"start":22845,"end":22890},"Das heißt, Du willst dort die Fehlerrate minimieren, dass das von dem von den Vorgaben abweicht.",{"sentences":28244,"speaker":620,"num_words":129,"start":22893,"end":22894},[28245],{"text":2480,"start":22893,"end":22894},{"sentences":28247,"speaker":935,"num_words":28258,"start":22896,"end":23185},[28248,28250,28252,28254,28256],{"text":28249,"start":22896,"end":22958},"Beim Reinforcement Learning ist es genau andersrum, Du hast diesen Grader, der dir 'n gewissen Reward gibt und versuchst diesen Reward zu maximieren.",{"text":28251,"start":22961,"end":23005},"Wenn man das jetzt in einfacher Sprache ausdrücken wollte, könnte man das Beispiel von Omas Rezept nehmen.",{"text":28253,"start":23007,"end":23114},"Wenn Du jetzt ein Model beibringen willst, wie Omas leckere Lasagne hergestellt wird, dann wäre der Vergleich beim Super Vice Feintuning, dass Du jeden einzelnen Schritt vom Model kontrollierst, dass es auch genau das macht, was Oma im Rezept hier gemacht hat.",{"text":28255,"start":23114,"end":23140},"Und immer wenn's leicht abweicht, gibst Du 'n gewissen Fehlerwert.",{"text":28257,"start":23143,"end":23185},"Beim Reanforcement Learning sagst Du, das muss genau so schmecken oder Du Du bewertest den Geschmack.",108,{"sentences":28260,"speaker":935,"num_words":28270,"start":23185,"end":23472},[28261,28262,28264,28266,28268],{"text":2480,"start":23185,"end":23188},{"text":28263,"start":23190,"end":23233},"Und Du lässt aber mittendrin das Model machen, was es will und Du versuchst es gar nicht einzuschränken.",{"text":28265,"start":23237,"end":23365},"Wenn der am Ende mit der Idee rauskommt, der schmeißt alles in die Luft und es landet Random Zeug in der Lasagne drin und Du schmeißt das zu 'ner Random Zeug in den Ofen und das ist aber leckerer als Omas Rezept, dann hat's 'n höheren Report und hat 'n besseren Weg gefunden.",{"text":28267,"start":23365,"end":23400},"Also so kann man die Sachen vielleicht vergleichen, Feintuning versus Reanforcement Learning.",{"text":28269,"start":23402,"end":23472},"Und ob Du nun willst, dass Du den Prozess 100 Prozent akkurat abbildest oder ob Du einen möglichst maximieren willst, das, ja, das hängt von deiner Domäne ab.",112,{"sentences":28272,"speaker":935,"num_words":26563,"start":23474,"end":23501},[28273],{"text":28274,"start":23474,"end":23501},"Aber die Models, die zur Verfügung stehen, die sind sehr generisch.",{"sentences":28276,"speaker":620,"num_words":129,"start":23506,"end":23507},[28277],{"text":2480,"start":23506,"end":23507},{"sentences":28279,"speaker":935,"num_words":28290,"start":23507,"end":23801},[28280,28282,28284,28286,28288],{"text":28281,"start":23507,"end":23580},"Die sind mit dem Weltwissen gefüttert und wenn Du die in deine Domäne reinpressen willst, wird es noch sehr lange Sinn machen, die auch für deine Fälle zu optimieren.",{"text":28283,"start":23583,"end":23640},"Also es ist richtig, dass Models so im Schnitt alle 7 Monate in ihrer Fähigkeit sich verdoppeln, immer immer besser werden.",{"text":28285,"start":23642,"end":23692},"Und ich weiß auch nicht, wo wir am Ende landen werden, ja, aber noch macht's auf jeden Fall Sinn, das zu trainieren.",{"text":28287,"start":23695,"end":23727},"Auch beim Thema Ivals, ja, nehmen wir mal an, die Models werden immer besser.",{"text":28289,"start":23730,"end":23801},"Haben jetzt Models, denen gibst Du eine Aufgabe, die arbeiten da eine Minute, anderthalb Minuten dran, bis dir Cloud Code Order Chat GPT da eine Antwort gibt.",113,{"sentences":28292,"speaker":935,"num_words":28057,"start":23803,"end":24062},[28293,28295,28297,28299,28301],{"text":28294,"start":23803,"end":23822},"Aber wie sieht das in 5 Jahren aus, ja?",{"text":28296,"start":23824,"end":23881},"Da hast Du dann Modelle, denen gibst Du eine Aufgabe und die arbeiten dann 16 Stunden da dran, bis die dir eine Ausgabe geben.",{"text":28298,"start":23881,"end":23918},"Und bei beim Testen von solchen long running Agents ist es halt sehr schwierig.",{"text":28300,"start":23918,"end":23944},"Du kannst den Prozess machen, den wir grade beschrieben haben, dieses.",{"text":28302,"start":23947,"end":24062},"Du guckst dir den Output an Richtung Reforcement Learning oder Du guckst dir den an, den kompletten Schritt, den das, den dein Agent unternommen hat und schaust dir den ersten Punkt an, wo er vielleicht vom Ideal abgewichen ist, Richtung, superweist Feintuning wieder.",{"sentences":28304,"speaker":935,"num_words":27166,"start":24062,"end":24141},[28305,28306],{"text":2480,"start":24062,"end":24065},{"text":28307,"start":24067,"end":24141},"Und da ist es natürlich superschwer, da die richtigen Entscheidung zu treffen und ja, wo der das, wo der Weg hingeht, das ist tatsächlich schwer zu sagen, jetzt Spannend.",{"sentences":28309,"speaker":129,"num_words":129,"start":24144,"end":24145},[28310],{"text":24142,"start":24144,"end":24145},{"sentences":28312,"speaker":150,"num_words":28083,"start":24148,"end":24181},[28313,28314],{"text":24142,"start":24148,"end":24149},{"text":28315,"start":24152,"end":24181},"Wärst Du gerne noch etwas gefragt worden, was wir dich nicht gefragt haben?",{"sentences":28317,"speaker":935,"num_words":26650,"start":24184,"end":24379},[28318,28320,28322,28324,28326],{"text":28319,"start":24184,"end":24225},"Ach, wir könnten noch so tief in die Thematik einsteigen, aber ich denke, es ist lustig.",{"text":28321,"start":24225,"end":24263},"Ich hab letztens Werbung für einen Film angezeigt bekommen, der nennt sich Mercy.",{"text":28323,"start":24267,"end":24340},"Und die Thematik fand ich superpassend, denn es geht in dem Film darum, dass k I, also wir haben ja vorhin über l m, also Judge gesprochen, Mhm.",{"text":28325,"start":24342,"end":24376},"Dass k I in der Zukunft tatsächlich als Judge im System eingesetzt wird.",{"text":2480,"start":24376,"end":24379},{"sentences":28328,"speaker":935,"num_words":26531,"start":24379,"end":24501},[28329,28331,28333,28334],{"text":28330,"start":24379,"end":24468},"Und ich hab dann, als ich den Trailer gesehen hab, mir gedacht, oh wow, wenn tatsächlich ein Large Language Model irgendwann rechtliche Entscheidungen trifft, wie würde man das denn testen und wie würde man das sicherstellen?",{"text":28332,"start":24471,"end":24489},"Das stell ich mir auch sehr, sehr schwierig vor.",{"text":698,"start":24493,"end":24494},{"text":28335,"start":24496,"end":24501},"I wird's.",{"sentences":28337,"speaker":129,"num_words":28347,"start":24504,"end":24642},[28338,28340,28341,28343,28345],{"text":28339,"start":24504,"end":24512},"Ja, da haben wir's.",{"text":698,"start":24515,"end":24516},{"text":28342,"start":24518,"end":24594},"Oh, ich ich ich würd mir jetzt sogar rausnehmen und sagen, das könnte man bereits schon machen und hätt eine sehr gute Abdeckung davon, wie 'n menschlicher Richter entschieden hätte.",{"text":28344,"start":24597,"end":24612},"Vielleicht Strafrecht ausgeklammert son bisschen.",{"text":28346,"start":24612,"end":24642},"So, ich glaub, Strafrecht ist hochindividuell, aber ich glaub, alles andere.",51,{"sentences":28349,"speaker":129,"num_words":620,"start":24646,"end":24651},[28350],{"text":28351,"start":24646,"end":24651},"Würd ja",{"sentences":28353,"speaker":150,"num_words":27547,"start":24651,"end":24688},[28354,28356,28357],{"text":28355,"start":24651,"end":24682},"auch viel gesagt, dass das so der nächste, eine der nächsten Branchen ist.",{"text":698,"start":24682,"end":24685},{"text":1288,"start":24685,"end":24688},{"sentences":28359,"speaker":935,"num_words":26700,"start":24690,"end":24707},[28360],{"text":28361,"start":24690,"end":24707},"Ja, ist ja deterministischer Prozess, ne.",{"sentences":28363,"speaker":129,"num_words":26625,"start":24707,"end":24742},[28364,28366,28368],{"text":28365,"start":24707,"end":24713},"Genau, genau.",{"text":28367,"start":24716,"end":24727},"Bis auf Strafrecht, glaub ich.",{"text":28369,"start":24730,"end":24742},"Strauchig ist glaub ich auch.",{"sentences":28371,"speaker":150,"num_words":27258,"start":24742,"end":24784},[28372],{"text":28373,"start":24742,"end":24784},"Und das ist natürlich dann auch hart, wenn man einen Film drüber macht in dieser aktuellen Zeit.",{"sentences":28375,"speaker":129,"num_words":26585,"start":24784,"end":24804},[28376],{"text":28377,"start":24784,"end":24804},"Ja, das ist Ja, ich mein, das",{"sentences":28379,"speaker":150,"num_words":935,"start":24804,"end":24812},[28380],{"text":28381,"start":24804,"end":24812},"ist irgendwie so.",{"sentences":28383,"speaker":129,"num_words":935,"start":24812,"end":24820},[28384],{"text":28385,"start":24812,"end":24820},"Ja, ja, okay.",{"sentences":28387,"speaker":935,"num_words":26976,"start":24822,"end":25017},[28388,28390,28391,28393,28395],{"text":28389,"start":24822,"end":24864},"Das ist aber auch wirklich so ein wirklich ein war, war in dem Moment auch so irgendwie Sand.",{"text":454,"start":24868,"end":24869},{"text":28392,"start":24869,"end":24883},"Ja, aber ist auch krass.",{"text":28394,"start":24883,"end":25013},"Ich hab auch letztens gelesen, Opus 4 Punkt 6 kam ja jetzt raus und es gibt ja so ein Benchmark, nennt sich Search Comp und die testen dort sehr, sehr schwierige Fragen, wo ein LLM die Aufgabe kriegt, wirklich so die Nadel im Heuhaufen im Internet zu recherchieren.",{"text":2480,"start":25016,"end":25017},{"sentences":28397,"speaker":935,"num_words":27529,"start":25019,"end":25093},[28398,28400,28402],{"text":28399,"start":25019,"end":25070},"Das sind wirklich, da gibt's nur eine einzige Seite, die die Antwort dafür liefert und ist das LLM in der Lage, das rauszufinden?",{"text":28401,"start":25073,"end":25089},"Das ist son sehr, sehr tricky Benchmark.",{"text":2480,"start":25092,"end":25093},{"sentences":28404,"speaker":129,"num_words":28103,"start":25093,"end":25176},[28405],{"text":28406,"start":25093,"end":25176},"Wo ich auch kurz den Sinn hinterfragen würde son bisschen, weil wenn ich gesagt hab, wenn jetzt nur einige eine einzige Quelle dafür gibt, so ist das wissen wir, wenn ich irritiert.",{"sentences":28408,"speaker":935,"num_words":27027,"start":25180,"end":25326},[28409,28411,28413,28415,28416],{"text":28410,"start":25180,"end":25219},"Ja, weil das die Challenge ist, ist es wirklich in der Lage, diese eine Quelle zu identifizieren?",{"text":28412,"start":25222,"end":25255},"Und tatsächlich ist es sehr interessant gewesen, Opus 4 Punkt 6 ist das erste Model gewesen.",{"text":28414,"start":25258,"end":25288},"Das hat identifiziert, dass es sich grade in der Evaluation befindet.",{"text":2480,"start":25288,"end":25291},{"text":28417,"start":25291,"end":25326},"Es hat nämlich gesagt, dass man kann ja dann den Thinking Step sehen.",{"sentences":28419,"speaker":935,"num_words":27291,"start":25326,"end":25465},[28420,28421,28423,28425,28427],{"text":2480,"start":25326,"end":25330},{"text":28422,"start":25330,"end":25382},"Hat gesehen, Moment, der User hat mich das gefragt, bla bla fing an, dann erst mal sonen Weg zu finden.",{"text":28424,"start":25384,"end":25421},"Und so mittendrin war in dem Thinking Step dann, das ist eine sehr, sehr spezifische Frage.",{"text":28426,"start":25421,"end":25451},"Die ist so hochspezifisch, befinde ich mich eventuell grade in einem Test.",{"text":28428,"start":25455,"end":25465},"Moment, ich recherchier das kurz.",{"sentences":28430,"speaker":935,"num_words":27274,"start":25469,"end":25684},[28431,28433,28434,28436,28438],{"text":28432,"start":25469,"end":25579},"Hat dann angefangen, diese Art von Fragen, die ihm gestellt wurde, im Internet zu suchen, ist in 'nem gelandet, hat rausgefunden, in welchem Benchmark es sich grade befindet, auf Basis auch der vorangegangenen Fragen, konnte aber die Antwort nicht sehen, die sind nämlich encodet.",{"text":2480,"start":25583,"end":25584},{"text":28435,"start":25586,"end":25630},"Hat sich dann das angeguckt, hat das Encoding geknackt und alle 1200 Antworten richtig submittetet.",{"text":28437,"start":25634,"end":25680},"So, jetzt könnte man sagen, der Benchmark war bestanden, denn es wurden alle 1200 Fragen richtig beantwortet.",{"text":2480,"start":25683,"end":25684},{"sentences":28440,"speaker":935,"num_words":27426,"start":25686,"end":25785},[28441,28443,28445,28447,28449],{"text":28442,"start":25686,"end":25707},"Die Frage ist, ist der Test jetzt noch valide?",{"text":28444,"start":25710,"end":25751},"Ist, ja, will man jetzt den Output greaten oder will man jetzt den Process greden, ne?",{"text":28446,"start":25751,"end":25762},"Das ist an Wow.",{"text":28448,"start":25766,"end":25780},"Der Stelle schwierig zu sagen.",{"text":698,"start":25784,"end":25785},{"sentences":28451,"speaker":935,"num_words":28083,"start":25785,"end":25827},[28452,28454,28456,28458],{"text":28453,"start":25785,"end":25796},"Ist eine coole Frage.",{"text":28455,"start":25796,"end":25803},"So Ja.",{"text":28457,"start":25806,"end":25824},"Awareness bin ich grade in 1, ja.",{"text":698,"start":25824,"end":25827},{"sentences":28460,"speaker":129,"num_words":26842,"start":25827,"end":26023},[28461,28463,28465,28467,28469],{"text":28462,"start":25827,"end":25921},"Aber ich, also da kam jetzt auch grade dieses Bild, was Du grade beschrieben hast, so voll in Kopf mit dieses, man wirft random Zutaten, holt irgendwie random mitm Ofen und zack, guckt, was dabei rauskommt.",{"text":28464,"start":25921,"end":25951},"Und zufällig ist das geiler als das, was Oma gemacht hat, so.",{"text":28466,"start":25951,"end":25985},"Da denk ich mir so, ja okay, also ist das reproduzierbar grade?",{"text":28468,"start":25985,"end":25998},"Ist das auf alles anwendbar?",{"text":28470,"start":25998,"end":26023},"Und das ist natürlich die Frage, die man sich stellen muss.",{"sentences":28472,"speaker":129,"num_words":26540,"start":26023,"end":26045},[28473,28475,28476,28478],{"text":28474,"start":26023,"end":26031},"Also deswegen, ja.",{"text":698,"start":26033,"end":26034},{"text":28477,"start":26034,"end":26039},"Oh, spannend.",{"text":28479,"start":26042,"end":26045},"Sehr gut.",{"sentences":28481,"speaker":150,"num_words":28492,"start":26045,"end":26282},[28482,28484,28486,28488,28490],{"text":28483,"start":26045,"end":26101},"Ja klar, wir könnten viel weiterreden, aber irgendwo ist auch der beste Podcast vorbei und auch damit unsere heutige Folge.",{"text":28485,"start":26101,"end":26173},"Von daher liebes Publikum, liebe Hörerinnen und Hörer, wenn ihr Feedback zu dieser Folge habt, dann schreibt uns gerne an Podcast at Programmier Punkt Bar.",{"text":28487,"start":26176,"end":26232},"Vielen Dank an dieser Stelle noch mal an die De Compide vielleicht da draußen, dass wir das im Rahmen machen konnten.",{"text":28489,"start":26232,"end":26258},"Vielen, vielen Dank Martin, dass Du heute Danke, danke.",{"text":28491,"start":26263,"end":26282},"Zeit gefunden hast, mit uns beide zu sprechen.",83,{"sentences":28494,"speaker":150,"num_words":27547,"start":26282,"end":26318},[28495],{"text":28496,"start":26282,"end":26318},"Hat uns sehr viel Spaß gemacht, war sehr informativ und wir machen Heli, Heli, Heli,",{"sentences":28498,"speaker":129,"num_words":26700,"start":26318,"end":26337},[28499],{"text":28500,"start":26318,"end":26337},"Heli, Heli, Heli, Heli, Heli, Heli,",{"sentences":28502,"speaker":935,"num_words":620,"start":26337,"end":26344},[28503],{"text":28504,"start":26337,"end":26344},"Heli, 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