eval-harness

Automated regression testing for AI model updates requires defining a structured eval suite with success criteria and running an evaluation harness against the target system to detect regressions. You compare current outputs against a baseline to track progress across release cycles.

AI capability assessment supports multiple grader backends, specifically code, model, and human graders. This flexible quality assessment approach allows teams to configure evaluations based on their specific metrics and testing requirements.

Aggregating and reporting evaluation metrics across experiments is achieved through the harness's centralized reporting feature. It surfaces trends and regressions by structuring schemas to codify tests, providing repeatable validation across codebases and release cycles.

Yes, the framework is explicitly applicable to both capability and regression evaluations across codebases, experiments, and release cycles. It enables teams to codify tests with structured schemas and track progress across iterations.

Yes, defining success criteria upfront is required before running AI evaluations. The framework solves the problem of providing a repeatable, scalable validation process by enabling teams to codify these tests using structured schemas before execution.