# Enterprise RAG: prepare a useful knowledge base

Prepare documents for enterprise RAG with a clear scope, access rights, traceable sources, updates and answer evaluation before rollout.

Author: Binov

Canonical: https://www.binov.com/en/guides/enterprise-rag-knowledge-base

A RAG project connects answer generation to information retrieved from a document collection. Quality depends on documents and access rights as well as the model. Before development, select a useful scope, identify content owners and gather questions whose answers can be checked.

## What RAG contributes

Retrieval-augmented generation finds relevant material and supplies it as context to a model. [Microsoft describes this principle](https://learn.microsoft.com/en-us/azure/search/retrieval-augmented-generation-overview). It does not guarantee accuracy: retrieval may miss a document, or the answer may misinterpret the material found.

For the user, the benefit is an answer with inspectable evidence. A citation should lead to a relevant passage and version, rather than merely opening a general folder.

## Review the collection before indexing

| Area | Useful question |
| --- | --- |
| Purpose | Which questions should the collection answer? |
| Ownership | Who confirms authoritative content? |
| Freshness | Which version applies? |
| Access | Which users may read it? |
| Quality | Is the text complete and usable? |
| Lifecycle | How do changes and deletions propagate? |

A large directory may contain duplicates, drafts and contradictory instructions. Making them all available without distinction leaves users to resolve those problems.

## Choose a coherent first corpus

Start with a collection whose audience and ownership are clear, such as a department’s procedures. Prepare frequent questions, ambiguous questions and requests the collection cannot answer.

For each expected answer, identify useful passages. This helps separate retrieval failures from writing failures and reveals information that is genuinely missing from the documentation.

## Design the answer and its boundaries

Provide a concise response, sources and a way to open the relevant document. Contradictory sources should be signalled. Missing evidence should lead to an explanation of the limitation rather than an invented completion.

Check permissions during retrieval and display. A summary can reveal restricted information even if the original document remains hidden. Include different access profiles and out-of-scope requests in evaluation.

## Plan updates

Define synchronisation triggers, deletion handling and ownership of obsolete material. Make the indexed version traceable. After a significant update, rerun the affected reference questions.

Collect useful feedback without unnecessarily copying sensitive content. A misunderstood question, a missing source and an incorrect interpretation require different fixes.

## Common questions

**Should we import everything immediately?** No. A controlled initial corpus lets you verify answers and permissions before expanding.

**Does RAG eliminate generation errors?** No. It supplies documentary context; results still need evaluation and visible limitations.

**Can it fit an existing application?** Yes, after reviewing authentication, data access and the user workflow.

Read about [integrating AI into an application](/en/guides/integrate-ai-existing-application) and [agent security](/en/guides/ai-agent-security-governance). Explore [Binov’s RAG development service](/en/ai-engineering/rag-development).