How to Build an AI Knowledge Base Your Team Can Trust

An AI knowledge base helps employees ask questions about company information in plain language. A common approach is retrieval-augmented generation, or RAG: the system finds relevant material and gives it to a model as context for an answer. Retrieval can improve relevance, but it does not guarantee that an answer is correct or that its sources are current. A folder called “Latest” can still contain a policy from three reorganizations ago.

Start with one set of questions

For an illustrative onboarding team, the first useful questions might be which documents a client must provide, who approves an exception, and what happens when a kickoff is postponed. That is a manageable starting point. Uploading every shared drive folder at once makes it harder to establish which document is authoritative and which questions the assistant should answer.

Choose a small, named collection with an accountable owner. Include the current process, exceptions, definitions, and links to the systems where the work happens. Record which questions are outside scope, such as interpreting an unsigned agreement or making a new policy decision.

Make each source maintainable

Design the answer and its fallback

1. 01

   Retrieve within the user’s access

   Filter the available information using application permissions before supplying it as context. A prompt alone should not decide which client files someone can see.

2. 02

   Show usable evidence

   Return source links and relevant sections alongside the answer. Check that the cited passage actually supports the advice.

3. 03

   Handle disagreement

   If two current documents conflict, show the conflict and identify the owner. Do not let the model invent a compromise.

4. 04

   Route unanswered questions

   Offer a clear handoff when evidence is missing. Save the question so the document owner can improve the knowledge base.

Test what the assistant should not answer

Create a small reference set from actual employee questions, with sensitive details removed where needed. Include an outdated document, a question with no answer, and two users with different permissions. A plausible response from an inaccessible source is a failure even if its wording is accurate. Microsoft’s RAG overview is useful background on retrieval architecture; the source ownership and test plan still need to fit your operation. Microsoft Learn reference

• Ask the same policy question before and after a source update.
• Remove access to a document and confirm it disappears from retrieval.
• Test a document that mentions another client by name.
• Ask for a recommendation that the source material does not support.

Track answer usefulness and missing knowledge

Measure supported answers, unanswered questions, corrections, and time spent checking sources. Review the queries that repeatedly escalate: they may reveal a missing procedure rather than a model problem. Keep operational decisions, such as approving a special exception, separate from the ability to explain an existing rule.