Prompt Evaluation: Versioning, Scoring and Drift

Prompt evaluation treats prompt edits, model changes, decoding adjustments, tool access changes, and provider updates as potential regressions by rerunning a fixed, versioned baseline suite before deployment. Generic instructions can improve one capability while severely harming another, as illustrated by a reported RAG compliance decline from 26/30 to 9/30, so prompt quality must be assessed against task-specific expectations rather than assumed from wording. Reproducible evaluation requires recording the prompt, exact model version, settings, reachable tools or sources, and the cases that approved the behavior, while baseline sets should emphasize real incidents, core user paths, and adversarial edge cases. Structural requirements such as JSON validity can use deterministic checks, but free-text qualities including grounding, relevance, and tone require scored evaluation and human review. Score aggregates may conceal movement in individual dimensions, judge selection can affect results substantially, and caching can hide model-driven behavior changes; therefore, teams should inspect sub-scores, version graders, disable caching for drift tests, and run scheduled evaluations even when no local prompt change occurred.

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