About This Course
Retrieval-Augmented Generation (RAG) is the most practical way to give LLMs access to your private data. This comprehensive course covers everything you need to build production-quality RAG systems.
Topics include document loading and preprocessing, chunking strategies, embedding models selection, vector database setup and optimization, retrieval algorithms, reranking, prompt engineering for RAG, evaluation metrics, and handling hallucinations.
You will build a complete RAG pipeline from document ingestion to answer generation, implementing best practices for each stage. The course covers advanced topics like hybrid search, multi-modal RAG, and self-correcting RAG systems that verify their own outputs.
