Rag Retrieval

This is the reference guide you want open when building production RAG systems. It covers the full stack: basic retrieval patterns, contextual retrieval (Anthropic's technique that cuts retrieval failures by 67%), HyDE for vocabulary mismatches, agentic workflows with self-correction, and pgvector hybrid search with proper HNSW indexing. The 30 rules are split across on-demand files, so you're not loading multimodal chunking logic when you just need basic embeddings. What I appreciate is the honest tradeoff guidance: it tells you hybrid search should be 40% BM25 / 60% vector, that HyDE needs a 2-3 second timeout with fallback, and why you'd pick text-embedding-3-small over voyage-3.5. Complexity is marked high because RAG done right involves real decisions about reranking, token budgets, and when to decompose queries.