RAG Best Practices (2026)
Ship reliable RAG: hybrid retrieval (BM25 + vectors), fusion + cross‑encoder re‑ranking, and evaluation that sticks.
By AI Mastermind Lab · Published 2 Mar 2026
Production RAG emphasizes retrieval first. A practical default:
Pipeline: ingest → chunk (256–512 tokens, slight overlap) → embed → hybrid retrieve (BM25 + vectors) → fuse (RRF/alpha) → re‑rank with a cross‑encoder → generate with citations.
Why hybrid: lexical recall (names, IDs) + semantic coverage beats either alone. Re‑ranking sharpens precision on the final set.
Evaluate: build a golden set; track Recall@k, MRR, nDCG; log latency per stage. Alert on recall drops.
Ops: refresh embeddings on content change; version both sparse and dense indexes; gate changes; add observability dashboards (overlap, hit@k, drift).
Security: sanitize retrieved text; defend against prompt‑injection; bound tool scopes.