# RAG Best Practices (2026)

> Ship reliable RAG: hybrid retrieval (BM25 + vectors), fusion + cross‑encoder re‑ranking, and evaluation that sticks.

Published: 2026-03-02  
Canonical: https://aimastermindlab.com/knowledge-base/rag-best-practices-2026

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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.
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Source: https://aimastermindlab.com/knowledge-base/rag-best-practices-2026 — AI Mastermind Lab (https://aimastermindlab.com)