evaluation-harness

To evaluate AI model outputs, you define golden datasets and metrics for classification, generation, and regression testing. The skill applies these criteria to non-deterministic prompts and model upgrades to generate a performance report.

LLM-as-judge is an evaluation mechanism that uses a language model to provide structured, auditable assessments of AI outputs. It judges baseline versus candidate model responses against predefined metrics on a representative dataset.

Regression testing for non-deterministic prompts uses golden datasets and metrics definitions to compare baseline and candidate outputs. This detects performance regressions across prompt changes, model upgrades, and retrieval strategies.

Yes, production monitoring is supported through asynchronous judgments. This allows continuous evaluation of live AI model outputs against defined metrics during pipeline changes and retrieval strategy updates.

The best way to compare model performance is running a baseline versus candidate evaluation on a representative golden dataset. This structured evaluation generates an auditable performance report using predefined metrics.

You need golden datasets for prompt testing when evaluating non-deterministic AI outputs. They provide the standardized reference data required to measure regression and validate model upgrades or pipeline changes accurately.