eval-harness

To prevent AI regressions in Gemini CLI workflows, implement eval-driven development by defining expected behavior and running repeatable capability and regression evals to track reliability metrics.

Eval-driven development for AI feature validation involves defining capability and regression evals before implementation, executing them, and tracking reliability metrics to ensure new improvements do not break existing behavior.

You measure pass@k and pass^k reliability metrics for AI outputs by running structured assessments using code, model, or human graders, then tracking performance across attempts and critical paths.

You set up regression testing for Gemini CLI sessions by defining expected behavior in an eval definition file with YAML frontmatter discovery metadata, then storing repeatable evals under the .gemini/ directory.

Yes, you can use different grading strategies for AI reliability evaluations by applying deterministic code graders, model-based graders, or human review markers to match varying risk levels.

The best way to generate structured reports for AI capability assessments is to use workflow templates that store eval definitions and automatically produce structured EVAL REPORTs for accountability and iteration.