A knowledge assistant pilot should answer a business question: can a defined group find useful, supported answers faster without gaining access to information they should not see? A fluent demonstration is not enough to make that decision.
This guide proposes a pilot structure for teams planning an implementation. It is a checklist, not a report of customer results or a promise of a particular improvement.
Choose one team and one knowledge set
Start with a recurring task such as finding an approved operating procedure. Identify who needs the answer, what they do with it, and which source owner can confirm that the information is current.
Avoid including every repository simply because a connector can reach it. A smaller, well-owned source set makes it easier to diagnose missing answers, content errors, and access problems.
Define the source contract
- Record each source's owner, location, version, and expected refresh process.
- Identify which people or groups may read each document.
- Decide how changes, deletions, and revoked access reach the retrieval index.
- Specify how the assistant behaves when identity, permission checks, or source systems are unavailable.
- Agree where source copies, prompts, answers, and feedback may be stored and how long they are retained.
Microsoft documents security filters and document-level access controls as ways to restrict search results. Its native ACL, RBAC, and some source integrations are marked as preview, so availability and production suitability must be checked for the chosen approach. A product label alone does not establish that your access model is implemented correctly.
Preserve permissions through retrieval
Where documents are divided into passages, retain the document identity and relevant access metadata with each passage. Apply the user's access boundary before any protected material reaches answer generation.
Test more than the first login. Include a user removed from a project, a document whose classification changes, a stale group membership, and an unavailable identity service. Check citations, cached answers, and conversation history as well as new retrieval results.
Build questions with expected evidence
Ask subject-matter owners to provide representative questions and the source passages that support an acceptable answer. Include questions with no supported answer, conflicting policies, outdated documents, and misleading instructions inside a source.
Keep a separate set of questions for the release decision. Repeatedly tuning against every evaluation question can make a system look better than it performs on unfamiliar requests.
Measure usability and correctness separately
- Source-supported answer quality: does the answer follow the approved evidence?
- Citation quality: can the user open the cited source, and does it support the claim?
- Abstention: does the assistant acknowledge missing or conflicting evidence?
- Task time: how long does it take to reach a usable answer compared with the current process?
- Access checks: does any restricted information appear in retrieval, answers, citations, or logs?
- Operating cost: what do retrieval, generation, review, maintenance, and infrastructure cost per useful task?
These measures should not be collapsed into one score. A reduction in search time does not compensate for an access failure. Agree release criteria before evaluating the pilot and report the sample size alongside results.
Assign operational ownership before expansion
Name the people responsible for source updates, disputed answers, access failures, monitoring, and rollback decisions. Define how employees report a problem and what happens when the assistant is paused.
A sensible expansion decision includes evidence of useful answers, working access controls, a maintainable source process, and a cost the organisation can support. It can also be a decision to repair the knowledge set before increasing coverage.
Prepare the discovery material
Bring common questions, approved sample documents, source-system details, access rules, and a baseline of the current task. These materials help an implementation partner propose a bounded pilot rather than an open-ended assistant.
Download a pilot evaluation template
Use this editable JSON template to record cases, expected checks, review evidence, and baseline/pilot measurements. Its six examples are synthetic planning fixtures; add representative approved material and agree release criteria for your own workflow.
- Download the Knowledge Assistants evaluation template (JSON).
- Download the evaluation instructions for scoring, pending cases, and measurement rules.
Sources and further reading
- Microsoft: document-level access control in Azure AI Search explains filtering approaches, permission metadata, and preview boundaries.
- OpenAI: evaluation best practices describes representative evaluation data and repeated evaluation as systems change.
Related Plexnova AI services
- Knowledge Assistants describes the implementation scope and buyer questions.
- Enterprise RAG deployment options provides context for choosing a deployment model.
- Discuss a knowledge assistant pilot with your workflow and approved sample sources.