What is prompt testing? Test cases, datasets, and regression checks explained

Prompt testing is a repeatable process for running a versioned prompt configuration, including its model, parameters, tools, output format, and retrieval context, against representative inputs with explicit expectations and consistent scoring to identify regressions before release. It evaluates task correctness, format compliance, safety boundaries, tool use, latency, and cost, using datasets that combine typical production requests, edge cases, known failures, and adversarial inputs; these datasets should be versioned and tagged to support reliable comparisons. Testing differs from broader prompt evaluation, production A/B testing, and deterministic unit testing, although all can complement one another. Scoring may use deterministic checks for schemas, labels, tool calls, and limits; model-based judges for qualities such as helpfulness or faithfulness; and targeted human review for calibration and disputed cases. Effective workflows compare saved experiment baselines at both aggregate and per-case levels, account for model variability through repeated trials, progress from exploratory playground testing to immutable experiment records and CI release gates, and turn traced production failures into future regression cases. The text presents Braintrust as a platform that unifies dataset management, scoring, experiment comparisons, CI integration, tracing, online evaluation, and cost monitoring, while noting that testing costs depend on dataset size, run frequency, scoring methods, model tokens, and retained trace data.

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