A company knowledge base is useful when employees can obtain an answer quickly and inspect its evidence. Uploading every file into a chat is insufficient. Select documents, assign owners and configure retrieval so that current policies do not blend with obsolete drafts.

What RAG means in practice

Retrieval-augmented generation combines document retrieval with answer generation. Relevant passages are found first and supplied to the model as context. The original Retrieval-Augmented Generation paper describes this approach. It does not mean automatically training a model on every company file, nor does it guarantee correctness. Retrieval-Augmented Generation · Lewis et al. ↗

A business interface should display the document name, version and supporting passage. A link to an entire folder leaves the employee doing the search again.

Example: travel approval

In a proposed onboarding workflow, a new employee asks who approves a business trip. The assistant retrieves the current procedure and identifies the responsible role. If two versions conflict, it exposes the disagreement and routes it to the policy owner rather than choosing whichever passage best matches the question.

This does not require the whole company archive. Start with one area, such as travel, purchasing or customer service. Define who maintains the material and how deleted documents are removed from retrieval.

Apply permissions before generation

The user should receive only passages they are allowed to access. Hiding a sensitive paragraph after it has already reached the model is inadequate. Test the same question as two employees with different permissions and verify that each answer uses the appropriate source set.

Evaluate retrieval separately from explanation

- Prepare questions with known answers and authoritative source passages. - Include questions that the collection cannot answer. - Test obsolete versions, similar titles and tables. - Score finding the right passage separately from explaining it correctly.

A retrieval failure and a reasoning error need different remedies. The former may need better document structure or indexing; the latter may need a narrower task, a different response format or another model. This makes the knowledge base easier to improve than simply feeding it ever more files.