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RAG: Retrieval-Augmented Generation

Die Architektur zuverlässiger KI

Robert Glaser, INNOQ, Marco Steinke, Hermann Schmidt, and Alexander Kniesz

In diesem Primer führen wir systematisch in die Konzepte und Architektur von RAG ein. Wir behandeln sowohl theoretische Grundlagen als auch praktische Implementierungsaspekte wie Chunking, Embedding und Vektordatenbanken. Außerdem teilen wir unsere Praxiserfahrung aus echten Projekten. Für Softwarearchitekt:innen und -entwickler:innen, die einen kompakten, aber fundierten Einstieg ins Thema suchen und den Einsatz von RAG in der eigenen Organisation bewerten wollen.

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Retrieval-Augmented Generation (RAG) ermöglicht es, die Fähigkeiten von Large Language Models für unternehmensinternes Wissen zu erschließen. Das ermöglicht nicht nur Antwortsysteme wie Chatbots, sondern auch die Umsetzung von zuvor schwierigen oder unmöglichen Features auf der Grundlage von internen, unstrukturierten Daten.

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Robert Glaser

Robert Glaser ist Head of Data & AI bei INNOQ. Mit einem Hintergrund im Software Engineering und einer Leidenschaft für benutzerfreundliche Webanwendungen begleitet er heute Unternehmen durch die KI-Landschaft. Dabei unterstützt er sie bei der Entwicklung von Strategien und Produkten für schwierige technische Herausforderungen. Fasziniert von der Vielseitigkeit generativer KI, moderiert er den Podcast „AI und jetzt“. Ihm ist die Brücke zwischen Technologie und Business ein Herzensanliegen. In seiner Freizeit erkundet er die lokale Food Szene.

INNOQ

We’re an IT consultancy with offices in Germany and Switzerland. We provide software architecture consulting, product development, and training services.

Marco Steinke

Marco Steinke works as Consultant at INNOQ, where he specializes in software architecture. His professional interests also encompass artificial intelligence, with a particular emphasis on the design and incorporation of AI systems.

Hermann Schmidt

Alexander Kniesz

Alexander started in 2017 as a working student. Since 2021 he now works as a consultant at INNOQ. Since his master’s degree in data science, his focus is on topics like data processing, machine learning, and AI. With those specializations, he is also interested in other software development topics to help bringing ML into production. Therefore he also assists in topics like Machine Learning Operations.

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