eval-harness

You can run regression tests for AI tasks by defining an eval workflow with formal success criteria. This framework measures performance and tracks progress across model versions to ensure consistent code assessment results.

Eval-driven development is a formal framework that replaces ad hoc evaluation by defining success criteria, measuring performance, and tracking progress across model versions for AI code tasks. It implements capability and regression eval types.

Yes, you can use a multi-grader approach for AI evaluation that includes code-based, model-based, and human review. This combination provides reproducible workflow metrics for iterative improvement and release readiness.

Define an eval workflow to evaluate Claude Code sessions by applying built-in graders. This creates a reproducible process with defined metrics and storage for iterative improvement and release readiness.

Yes, the evaluation framework supports both capability and regression eval types for AI development. It provides metrics and workflows designed to measure performance and track progress across model versions.

AI task evaluation is inconsistent when using ad hoc methods. You can solve this by applying a formal, criteria-driven framework that implements defined metrics, multiple graders, and a reproducible workflow.