eval-harness

Regression evals validate that existing AI functionality remains intact after code changes. You define an evaluation suite and execute it to automatically track performance against established baselines.

Eval-driven development formalizes AI task validation by defining repeatable test suites. Pass@k metrics measure the probability that a correct response appears within k generated attempts, enabling precise model benchmarking.

You define an evaluation using a specific command to create the test parameters, then execute it with a check command to run the suite and generate standardized quality reports for your prompts.

Yes, this supports model benchmarking across different AI versions. It applies capability and regression eval types to compare performance shifts and track baseline metrics over multiple model iterations.

Automating AI regression testing requires a dependency-free harness that defines test suites and executes them automatically. This generates standardized evaluation reports and tracks pass@k metrics for quality assurance.

Automated eval suites are not suited for highly subjective prompt outputs lacking definable pass criteria. They require clear baseline metrics and structured pass@k parameters to effectively measure AI task performance.