Retrieval-augmented generation, or RAG, brings relevant information into a model’s context before it produces an answer. For an enterprise knowledge assistant, that information might come from approved manuals, policies, or product documentation.
Connecting a search index to a language model is only part of the work. The experience also needs to help people understand which source supports an answer, whether it is current, and what the system cannot establish.
Treat the knowledge collection as a product
Prepare documents for retrieval by preserving useful structure and dividing long material into meaningful passages. Keep titles, source locations, and document metadata with those passages so the application can reconstruct context.[1]
Microsoft’s RAG guidance covers document preparation, chunking, and search design. In practice, start with a manageable collection and inspect what the ingestion process actually produces before adding more content.
Retrieve evidence that answers the question
Keyword search can help with exact identifiers, while vector search can help with differences in phrasing. Hybrid search combines the approaches. Ranking and filtering determine which passages reach the answer-generation step.[1]
Inspect the retrieved passages before changing the writing prompt. If the needed evidence is absent, a more confident instruction to the model will not repair the missing context.
Make permissions and citations part of the flow
Enforce access rules when selecting source material. Include the document identity and passage location with the retrieved context, then present references alongside the answer. A citation should let a reader inspect the supporting evidence.[1]
Design a response for missing or conflicting material. The assistant can identify the gap, ask a narrower question, or route the question to a content owner. It should not treat an empty search result as evidence that a policy does not exist.
Evaluate retrieval and answers separately
A useful diagnosis separates search quality from answer quality. Retrieval evaluation asks whether the system found relevant evidence. Answer evaluation examines whether the response addresses the question and stays supported by the supplied material. Microsoft documents measures for these different layers.[2]
Use questions with known supporting passages, questions requiring multiple sources, and questions that the collection cannot answer. A citation’s presence alone does not prove that the cited text supports every claim.
- Retrieval relevance: did the necessary passages appear?
- Groundedness: are the answer’s claims supported by the retrieved context?
- Completeness: did the response cover the important parts of the question?
- User experience: can a reader verify the answer and recover from a failure?
Plan for change after launch
Assign ownership for source updates, document retirement, and access changes. Preserve a small regression set of important questions and rerun it when the knowledge collection or application configuration changes.
Review unanswered questions with the people who maintain the content. Sometimes the right fix is a clearer document or a missing procedure. Treat these findings as knowledge-management work as well as application feedback.
PUT IT INTO PRACTICE
A dependable RAG experience makes the evidence inspectable. Improve the content, retrieval, and answer behavior as separate parts of one system.