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RAG: Retrieval-Augmented Generation
Die Architektur zuverlässiger KI
This book is 100% completeLast updated on 2024-11-25
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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About the Book
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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About the Authors
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.
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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