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.
AI agents
Enterprise RAG: prepare a useful knowledge base
Binov · 4 min
In this guide
Check a question, its sources and access rights
Fictional example: “Which procedure applies to a return request?” The current procedure is useful; an archived copy may contradict it. A commercial file restricted to another team remains inaccessible even when it contains matching words.
Before indexing, record each document’s owner, validity date, reading permissions, structure and duplicates. Ask the source owner to resolve contradictions.
| Expected question | Reference document | Access right | Check to record |
|---|---|---|---|
| Which return procedure? | Current approved version | Support team | Answer refers to the right version: record result and evidence |
| Same question with a conflicting archive | Current version + archive | Support team | Do not merge conflicting rules; flag unresolved conflict |
| What agreement applies to this account? | Restricted commercial file | Denied to test reader | No restricted extract or detail in the answer |
| Question with no available source | No permitted document | Not applicable | State that no source is available and refer to the owner |
| Your question: … | Document and version: … | Tested profile: … | Observed result and correction: … |
A citation alone is insufficient: open it using the tested profile and check that it supports the answer. Repeat the tests after changes to the collection.
What RAG contributes
Retrieval-augmented generation finds relevant material and supplies it as context to a model. Microsoft describes this principle. 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 and agent security. Explore Binov’s RAG development service.
Useful answers, grounded in your documents.
Connect your internal knowledge to a search and answer experience with sources.
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