What Is RAG? Using AI with an Enterprise Knowledge Base

When an employee asks about maintenance precautions for a particular model, a general language model may not know the latest internal manual. Retrieval-augmented generation, or RAG, first retrieves relevant material from designated sources and supplies it to the model as the basis for an answer.

Its value lies in bringing company documents into the answering process. Connecting a knowledge base does not, however, guarantee correctness.

1. How RAG works

A typical workflow has two parts. During preparation, the system reads documents, organizes their text, divides it into searchable sections, and builds an index. When a user asks a question, it retrieves relevant sections and gives them to the model together with the question.

Retrieval may use keywords, vector similarity, or a combination. Each method should be tested against your own data, particularly model numbers, codes, and specialized terms, rather than judged only on general questions.

2. How it differs from retraining a model

RAG supplies external information at answer time; it does not mean retraining the model on every document. After a document changes, updating the retrieval data can make the new content available to subsequent answers, provided the index has finished synchronizing.

When a new manual takes effect, decide whether the old version remains available for historical cases and which version should be preferred by default. Placing both files in a folder is not a sufficient version policy.

3. Document quality determines what can be found

Poor scans, broken tables, missing section titles, and contradictory rules across documents can all affect results. Retain document names, versions, intended audiences, publication status, and source locations during preparation.

If a precaution is separated from its equipment model during document splitting, the system may retrieve the right words for the wrong situation. Keep enough context in each section and validate the approach with real questions.

4. Filter permissions before providing content

A question-and-answer interface must not expose documents a user cannot otherwise access. Enforce restrictions in retrieval and backend processing rather than relying on a prompt telling the model not to disclose information.

Query histories and caches also need appropriate separation. A complete answer produced for one user should not be reused for another user with different permissions.

5. Show sources and verify that they support the answer

Document links help readers check an answer, but a citation is not proof of accuracy. The system may still combine passages incorrectly or present a recommendation as a mandatory rule.

When information is insufficient, sources conflict, or the applicable version cannot be found, the system can state that it cannot confirm the answer and refer the question to the responsible person. Text inside a document that tells the system to ignore rules or perform an action should remain source material, not become an instruction.

6. Testing an enterprise knowledge base

Include questions with answers, questions without answers, identical names across different models, outdated rules, and access-boundary cases. Check retrieval of the correct source separately from whether the final answer faithfully reflects it.

Also test additions, revisions, and withdrawals. An update is not complete if deleted knowledge continues to appear through cached answers.

Start with one document category that has clear ownership and an update process. RAG often succeeds or fails on whether information can be maintained, validated, and refreshed—not on the model alone.

Further reading Microsoft Learn: Retrieval augmented generation and indexes https://learn.microsoft.com/en-us/azure/foundry/concepts/retrieval-augmented-generation