Shipping RAG to Production: The Checklist Nobody Gives You
Retrieval-augmented generation demos are easy; reliable production systems are not. Here's what actually breaks and how to prevent it.
The demo-to-production gap
A weekend RAG demo answers questions impressively. Then real users arrive with ambiguous queries, stale documents, and adversarial inputs, and quality collapses. The gap is rarely the model — it is retrieval quality, evaluation, and operational discipline.
Retrieval is the product
Invest in chunking strategy, hybrid search (semantic + keyword), and metadata filtering before touching prompts. Measure retrieval hit-rate independently of answer quality: if the right passage never reaches the context window, no prompt engineering can save you.
Evaluate like you mean it
Build a golden set of real questions with reviewed answers, and score every pipeline change against it — faithfulness, relevance, and refusal correctness. Automated LLM-as-judge evaluation catches regressions cheaply; periodic human review keeps the judge honest.
Guardrails and observability
Log every query, retrieval set, and response with privacy controls. Add input filtering, output validation, and confidence-based fallbacks to human support. A RAG system without tracing is a black box you will debug in production, in front of your users.
