An enterprise knowledge assistant must retrieve the right documents for the right person. Improve freshness, source grounding and access control before expanding it.
Retrieval quality is a data problem
If a policy assistant answers from an expired procedure, changing the prompt alone will not repair the source pipeline. Track document owners, effective dates and update events. Decide how old versions are removed or marked, and how quickly edits reach the search index.
Chunking also needs a purpose. A fragment separated from a table header or exception can lose the context required to answer correctly. Test representative questions against the retrieved passages before judging the model’s wording.
Enforce permissions before generation
A restricted document should not enter the context for someone who lacks access. Microsoft’s search-security guidance describes document-level authorization as a distinct control. Do not rely on instructions telling a model to hide information that it has already received.
Test with multiple identities, including users with revoked access and people belonging to more than one group. Verify that citations themselves do not expose restricted titles, paths or snippets.
Make uncertainty usable
Return a clear limitation when the sources do not support an answer. Let users inspect the relevant evidence and report outdated material. Separate source freshness, retrieval relevance and answer quality in your evaluation. A system can fail one of these while appearing strong on the others.
A practical next step
- Assign owners to the most important knowledge collections.
- Test removal and permission-change propagation.
- Evaluate retrieval with realistic user identities.
- Track incorrect answers with their retrieved evidence.
Primary reference
Microsoft Azure AI Search security guidance. Product features and availability can change; check the relevant documentation for your environment.
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