State the decision the pilot supports
Write the commercial question before testing. For example: can this experience help a defined audience understand a reviewed programme and reach the official next step? That keeps the evaluation tied to a real service outcome.
Build a representative question set
Include routine questions, paraphrases, ambiguous requests, edge cases, out-of-scope topics and questions whose answer is deliberately absent. Ask content owners and candidate-facing teams to review the set before it becomes evidence.
Review more than correctness
For each response, consider source support, relevance, clarity, boundary behaviour and next-step routing. A fluent answer can still be unsupported; a safe refusal can still be unhelpful if it omits an available official route.
Bind the evidence to one revision
Record the exact content and configuration revision, evaluation set and result. If the sources, prompt, model settings or intended capabilities change, the previous result does not automatically approve the new revision.
Keep manual review and aggregate reporting distinct
Manual evaluation can inspect exact agreed questions and answers. Live aggregate reporting, where enabled and verified, shows bounded themes and outcomes rather than exact question text. It can suggest where to investigate but cannot diagnose the cause by itself.
Define the next acceptance step
A pilot result may justify revision, a wider test or a production-readiness review. It does not itself authorise public deployment. Privacy, security, accessibility, operations and release approval remain separate gates.
Further reading
- NIST AI Risk Management Framework. Govern, map, measure and manage AI risk.
- Roleline trust and boundaries. The product and deployment claims used on this site.