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FROM NAIVE TO ADVANCED RAG, THE COMPLETE GUIDE Google Cloud
It’s easy to get started with Retrieval Augmented Generation, but you’ll quickly be
disappointed with the generated answers: inaccurate or incomplete, missing context or
outdated information, bad text chunking strategy, not the best documents returned by
your vector database, and the list goes on.
After meeting thousands of developers across Europe, we’ve explored those pain
points, and will share with you how to overcome them. As part of the team building a
vector database we are aware of the different flavors of searches (semantic, meta-data,
full text, multimodal) and embedding model choices. We have been implementing RAG
pipelines across different projects and frameworks and are contributing to LangChain4j.
In this deep-dive, we will examine various techniques using LangChain4j to bring your
RAG to the next level: with semantic chunking, query expansion & compression,
metadata filtering, document reranking, data lifecycle processes, and how to best
evaluate and present the results to your users.