Ai Rag

This is a complete RAG implementation reference covering chunking strategies, hybrid retrieval (BM25 + vector search), reranking, and evaluation metrics like nDCG. It pushes you toward separating retrieval quality from answer quality, treating staleness as a bug not a UX issue, and instrumenting every pipeline stage. The decision trees are practical: pick chunking by document type, add reranking when you have noisy results, choose indexes by dataset size. Best part is the grounding section treating retrieved text as untrusted input with citation coverage checks. Use this when building production RAG systems where hallucination and stale results actually matter, not prototypes.