eval-harness

AI task validation is the systematic process of assessing and certifying AI assistant sessions to ensure measurable reliability. You need it to establish repeatable QA standards, applying explicit success criteria and baselines to guide release decisions for model versions.

To run regression evals for AI prompts, define a feature with explicit success criteria and launch the evaluation pipeline. The framework applies deterministic code-based graders and model-based rubrics to collect results and quantify performance across prompt changes.

Yes, the framework supports multi-grader configurations allowing you to combine deterministic code-based graders and model-based rubrics. You can also extend the evaluation process with optional human reviews to score prompts, tools, and interactions robustly.

You define capability and regression criteria by creating an evaluation definition that outlines explicit success conditions for your AI tasks. This standardizes evaluations by applying consistent pass@k metrics, baselines, and auditable run histories across different model versions.

Yes, the evaluation framework supports optional human reviews alongside automated graders. You can extend the evaluation pipeline across prompts, tools, and interactions by adding human reviews to complement deterministic code-based and model-based scoring for robust certification.

You need explicit success criteria and baselines for AI task validation to generate auditable reports that guide release decisions. Establishing these requirements ensures measurable reliability and enables accurate comparisons between different model versions and prompt iterations.