Evaluation measures how a configured AI experience performs on a defined task. A result can reflect the model, retrieved information, access, instructions, or tool behavior; it is not necessarily a verdict on the model alone.
Define the unit of success.
An evaluation needs a task, an expected behavior, and evidence for judging the result. For a drafting task, usefulness and correction effort may matter most. For information retrieval, source relevance and permission boundaries are central. For an action-taking agent, correctness includes the effect of the tool call, not only the wording of the answer.
Separate quality, security, and operational measures. A fast response can still be wrong; a correct response can still reveal information to the wrong person. A single average score can hide failures that should block release.
Build representative cases.
Collect examples that reflect real user roles, information quality, and task variation. Include missing evidence, contradictory sources, long requests, unsupported instructions, and access the user should not have. Use approved or synthetic data and document how each case should be assessed.
Keep a stable regression set for comparisons and reserve additional cases for independent review. Repeatedly tuning to the same examples can improve the score without improving behavior outside that narrow set. Record which configuration and sources produced each result.
Example: answering a policy question.
A useful case might ask about expenses for a particular location and employment type. The expected answer should use the current applicable policy, identify a genuine exception, and avoid inventing missing rules. Reviewers can inspect the retrieved passages separately from the generated answer.
A wrong answer with a correct source suggests a different failure from a wrong answer caused by retrieving the wrong policy. This separation helps determine whether to improve content, search, instructions, or model configuration instead of changing everything at once.
Turn results into a release decision.
Combine automated checks with human review of consequential or ambiguous outcomes. Record failed cases, severity, owners, and required remediation. Compare latency, cost, and user correction effort alongside quality. A pilot should end with a documented decision to proceed, refine, or pause, with repeat evaluation triggered by meaningful changes.
Further reading
For selecting pilot tasks and deciding whether to expand, read Copilot and data readiness. For release boundaries and approval decisions, read AI Governance.