Retrieval-Augmented Generation in Production

Retrieval-augmented generation combines search with language generation, but production quality depends on the entire path from source documents to the final answer. Ingestion, parsing, chunking, metadata, permissions, ranking, prompting and evaluation all contribute to the result.

A strong model cannot recover facts that were removed during parsing or retrieve material hidden by weak metadata. Begin by making the knowledge pipeline observable and testable.

Retrieval should respect the same access rules as the source system. Permission filtering must happen before context reaches the model. Each result should carry enough provenance to identify its source, version and relevant location.

Hybrid retrieval often works better than a single technique. Keyword search protects exact names and identifiers, while semantic search captures related language. Ranking can then combine relevance, freshness, authority and user context.

Build an evaluation set from questions users actually ask, including ambiguous, unanswerable and permission-sensitive cases. Score retrieval separately from answer quality so the team can see whether a failure began in search or generation.

Production systems need a safe no-answer path, citations that users can inspect, feedback capture and monitoring for source changes. RAG earns trust by exposing evidence and uncertainty.