Retrieval-augmented generation, or RAG, retrieves relevant information and provides it to an AI model when answering a question. For a business, it can connect an assistant to maintained policies, manuals and service information. Its value depends on retrieving the right evidence for the right person; connecting a folder alone does not make the answers dependable.
How does a RAG knowledge base work?
The system searches approved content, selects relevant passages and asks a model to answer using that context.
Microsoft's Azure AI Search documentation describes retrieval as the grounding stage of a RAG application. Search and answer generation are separate responsibilities. A fluent response cannot compensate for retrieving the wrong policy, and a good search result still needs to be represented accurately in the answer.
Imagine an internal assistant answering a question about equipment eligibility. It should retrieve the applicable policy version, identify the relevant conditions and point the employee to that source. If eligibility also depends on a live employee record, the document alone cannot establish the final answer.
Sources: Microsoft Learn — Retrieval-augmented generation overview
Which information belongs in the knowledge base?
Start with a narrow collection of approved documents whose authority, audience and update process are clear.
Our suggested source register separates what a document says from whether it should be used. Give every source an owner, a stable identifier, an effective date, a status and an intended audience. Resolve contradictory instructions before indexing them. Including both an obsolete handbook and its replacement can create an answer that combines incompatible rules.
Choose content based on real questions. For a support assistant, the useful starting set might be troubleshooting instructions, product limitations and escalation rules. Archived sales conversations can contain promises that were exceptions or mistakes; they should not automatically become policy.
| Source | Required control | Example failure to prevent |
|---|---|---|
| Service handbook | Approved version and review owner | An answer based on a retired service |
| Customer contract | Customer and role restrictions | One account seeing another account's terms |
| Product manual | Model and revision identifiers | Instructions for the wrong model |
| Live operational record | Authorised current-system lookup | A stale document presented as live status |
How do permissions work in a RAG application?
Restrict retrieval to content the requesting user is allowed to access, before supplying it to the model.
Microsoft documents document-level controls for restricting search results. The exact mechanism depends on the selected platform and data source. The application still needs a trustworthy user identity and a tested mapping from source permissions to search access. A prompt saying not to reveal confidential information is not that mapping.
For an illustrative multi-client consultancy, test the same question as a project member, a finance user and a user from another client account. Check both the answer and the cited documents. Also test shared conversation history and cached answers: an earlier authorised response should not become a shortcut around a later user's access checks.
Make permission changes part of the refresh design. Removing access in the source system must have a defined effect on search and stored context. Decide how quickly revocation must take effect and what the assistant does while that state is uncertain.
How should you test answers and source updates?
Test whether the evidence is relevant, the answer is supported and the system handles missing or changed information honestly.
Prepare questions from actual support or internal-service work. For each one, record the document that should support the answer, the essential conditions and what would make the answer misleading. Include questions with no approved answer, conflicting documents and information the test user cannot access.
Then change a source deliberately. Replace an approved procedure, withdraw a document and rename a product. Confirm that retrieval and citations reflect the change. A citation to a real page is insufficient if that page no longer supports the statement.
Measure retrieval misses separately from incorrect summaries. The first may require changes to source preparation or search; the second may require a clearer response policy or a different generation approach. Keeping the failures distinct prevents endless prompt changes from masking a data problem.
When is a knowledge assistant worth building?
Build when repeated information-finding work has meaningful cost and the business can maintain the underlying sources.
Begin with one audience and one document collection. Compare the assistant with an improved search page or a better-organised handbook. If users can already find the answer quickly, a generated response may add cost and verification effort without solving a real problem.
RAG supplies context; it does not automatically authorise actions. An assistant that explains a policy does not need permission to edit employee records or approve a refund. Treat any later tool access as a separate design decision with its own acceptance criteria.
Common questions
Does RAG eliminate hallucinations?
No. Retrieval may return irrelevant or incomplete information, and the model can misrepresent what it receives. Test grounding, show useful sources and define when the assistant should say it cannot establish an answer.
Does connecting documents retrain the model?
In a typical RAG system, retrieved text is supplied as context when a question is answered. That is different from updating model weights through training. Confirm the actual data handling and retention terms of the services you use.
Can a RAG assistant use live CRM information?
It can be designed to retrieve authorised live records through an integration. Separate those lookups from static document search, and preserve the CRM's access rules and the freshness needed for the question.