eval-harness

Regression testing for AI prompts ensures prompt or model changes do not degrade existing behavior. You define regression evals with specific criteria, and the harness executes them to track pass/fail metrics across different versions.

You configure graders within the evaluation framework to execute tests and generate reports. The harness supports code-based, model-based, and human graders to evaluate test results and generate standardized, reproducible reports.

Yes, you can use code-based, model-based, and human graders together in Claude Code sessions. The evaluation framework supports multiple grader types simultaneously to provide flexible test tracking and standardized reporting.

You track pass/fail metrics across model versions by running the evaluation harness with your defined capability and regression evals. The framework stores eval data centrally to generate reproducible reporting and compare results.

The best way to prevent regressions when updating AI prompts is implementing regression evals. The evaluation framework executes standardized tests using various graders to track pass/fail metrics and ensure changes maintain behavior.

You need a formal evaluation framework for regression testing AI prompts to achieve reproducible reporting and centralized eval storage. It standardizes grading across code, model, and human evaluators to reliably track metrics.