RAG in Production: A Practical Guide
Blueprint for reliable RAG: chunking, hybrid search, cross‑encoder re‑ranking, evaluation sets, and observability.
By AI Mastermind Lab · Published 1 Mar 2026
Most "RAG problems" are retrieval problems. Treat retrieval as a first‑class product surface.
Architecture: documents → chunk & embed → vector DB → retrieve top‑k (hybrid) → re‑rank → prompt LLM with citations.
Design: start with HNSW indexes; apply metadata filters before vector search; add cross‑encoder re‑ranking for precision.
Evaluation: curate a 50–100 example golden set; report Hit@k, MRR, NDCG and annotate failure modes. Re‑run on every change.
Observability: track p95 latency, cache hit rate, and retrieval overlap; alert on drops in hit rate or sharp latency spikes.
Governance: guard against prompt injection by sanitizing retrieved text and limiting tool scopes.