A comprehensive guide to building and optimizing Retrieval-Augmented Generation (RAG) systems in production. Covers the full RAG pipeline from data ingestion, chunking, and indexing to retrieval, prompting, and evaluation. Includes practical advice on improving retrieval relevance using hybrid search, metadata filtering,
Table of contents
What is RAG in AI?What is retrieval-augmented generation in generative AI?How do you build a RAG pipeline?How do you improve RAG retrieval relevance?How do you evaluate RAG quality?What are common RAG mistakes?How do you secure data in RAG?How do you keep RAG results up to date?How does Meilisearch fit into a RAG system?What to do next with RAG in AISort: