An evaluation set should represent the decisions the product actually makes. Mixing harmless wording differences with unsafe tool calls in one average can conceal a release blocker. An <a href="https://clutch.co/profile/pharos-production">AI evaluation framework</a> can assign each test case to a named failure mode.

Start with expected behavior, allowed variation and the condition that should fail the case, then keep retrieval, reasoning, formatting and tool execution results separate so a team can locate the regression. https://clutch.co/profile/pharos-production

A <a href=https://clutch.co/profile/pharos-production>production AI testing strategy</a> also needs fixed comparison data for model or prompt changes. Human review is useful for disputed cases, but reviewers need the same rubric. Otherwise the evaluation measures reviewer preference rather than product behavior.