Enterprise RAG: Building AI That Answers from Your Knowledge

Retrieval-Augmented Generation, or RAG, lets an AI assistant answer from approved organisational knowledge. It is a strong fit for policies, procedures, product documentation and internal research—provided the retrieval and access-control layers are engineered carefully.

Treat content quality as system quality

Duplicate, outdated or contradictory documents create unreliable answers. Establish ownership, freshness rules and source priorities before ingestion. Metadata such as department, region, document type and effective date makes retrieval more precise.

Retrieval needs its own evaluation

Chunking and embedding are only a starting point. Test keyword, vector and hybrid retrieval against real questions, then inspect whether the correct evidence appears near the top. Reranking often improves results for complex or ambiguous queries.

Permissions must follow the source

A knowledge assistant should never reveal content a user cannot access directly. Apply source-level permissions during retrieval, pass user identity through the system and log the documents used for every answer.

Make uncertainty visible

Require citations, define when the assistant should say it cannot answer and provide a path to a subject expert. Measure groundedness, citation accuracy and unanswered-question quality over time.