What makes a RAG system production-ready?
Retrieval-augmented generation becomes valuable when it can find the right evidence, respect access boundaries, and make uncertainty visible. Production readiness is less about a clever prompt and more about a dependable system around the model.
Retrieval quality comes first
Chunking, metadata, hybrid search, reranking, and source freshness determine whether the model receives useful evidence. Measure retrieval separately from generation so you know which layer needs work.
Design for trust
Show citations, preserve document permissions, define refusal behavior, and route sensitive actions through human review. A useful assistant should make its evidence and boundaries understandable.
Evaluate the real workflow
Build a representative question set from user behavior. Track answer relevance, groundedness, retrieval coverage, latency, and cost, then use production feedback to improve each layer deliberately.