How to plan an enterprise RAG pilot your team can evaluate

An enterprise RAG pilot should answer a practical question: can a defined team find and use the information it needs more effectively with this application?

Retrieval-augmented generation connects a language model to selected information sources. The application retrieves material relevant to a question and uses it when preparing an answer. Making that useful in a company requires decisions about the collection, permissions, user experience and evaluation.

Choose one team and one knowledge problem

Start with a group that can describe recurring questions and judge the answers. A support team looking for approved product guidance has a different knowledge problem from an engineering team searching technical records.

Write down what the pilot should help them do. Examples include locating the current procedure, finding a contract clause or assembling relevant material for a draft. Also define what it will not cover yet, so that the first evaluation has a clear scope.

Record the current process. Ask users how they find an answer today, what slows them down and how they decide whether a source is reliable. Those observations provide a useful baseline for the pilot.

Select sources with an owner

Choose a manageable collection with a person responsible for its content. Review document formats, duplicates, version history and whether important information is contained in scans, tables or attachments.

Agree on which documents are authoritative. If two sources conflict, the application needs a way to expose that conflict or prefer an agreed version. Indexing more material will not resolve an unclear ownership or approval process by itself.

Plan how changes enter the index and how deleted documents are removed. The pilot should test an update to an existing source, not just a collection that never changes.

Build the evaluation questions before tuning

Ask the pilot team for representative questions and the material that supports a useful answer. Include variations in terminology and questions whose wording differs from the source.

Add cases with no answer in the collection, incomplete information and conflicting documents. These examples help evaluate whether the application explains uncertainty instead of presenting an unsupported response.

Keep some examples separate from the set used while developing the application. That gives the team a more useful check on whether improvements carry over to unfamiliar questions.

Evaluate retrieval and answers separately

When an answer is weak, first check whether the relevant source was retrieved. If it was missing, changing the model’s instructions may not fix the underlying search problem. Review document extraction, segmentation, metadata and the retrieval query.

If the correct source was available, inspect how the application used it. Did the response preserve the relevant conditions? Did it distinguish separate cases? Do the cited passages support what the answer says?

Agree on a simple review rubric with the users. It can distinguish a usable answer, an answer needing correction, a missing answer and an unsupported claim. Keep the examples and reviewer comments so that changes can be compared consistently.

Test permissions as a user experience

Create test accounts with different access rights and include questions about restricted documents. Verify that retrieval uses only permitted material and that source links respect the same boundaries.

Test what happens when a person’s access changes or a document is removed. Consider what the application stores in conversation history and logs, because those surfaces also need an agreed access and retention approach.

The interface should make it easy to open a source and recognize when available information is insufficient. A citation is a useful review tool; it is not proof that the generated answer is correct.

Include operation in the acceptance criteria

Evaluate response time with a representative workload, then check the refresh process, error reporting and recovery responsibilities. Determine who will respond when a source connection stops working or users report an incorrect answer.

Before expanding, review quality, usability, permissions and operating effort together. A pilot can justify rollout, identify changes needed for another evaluation or show that the selected use case is not ready.

Our RAG and enterprise search service covers the knowledge application and its evaluation. Business system integrations can bring it into existing tools, while managed AI services support the application after launch.