# RAG in Production: A Practical Guide

> Blueprint for reliable RAG: chunking, hybrid search, cross‑encoder re‑ranking, evaluation sets, and observability.

Published: 2026-03-01  
Canonical: https://aimastermindlab.com/knowledge-base/rag-in-production-guide

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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.
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Source: https://aimastermindlab.com/knowledge-base/rag-in-production-guide — AI Mastermind Lab (https://aimastermindlab.com)