My notes for the Ultimate RAG Bootcamp (LangChain · LangGraph · LangSmith) — organized section-by-section exactly like the course, from RAG foundations to agentic, corrective, adaptive RAG and GraphDB. Updated as I progress.
Combining dense (vector) and sparse (BM25) retrieval with an ensemble, then sharpening results with cross-encoder reranking and diversifying them with MMR.
Reshaping the user's query before retrieval — query expansion (synonyms/context), query decomposition (split complex questions), and HyDE (embed a hypothetical answer) — so the retriever finds better context.