AI Model Evaluation
Definition
AI model evaluation is the process of measuring how well a model performs against a defined standard, using test data and criteria that reflect the real task the model is meant to do.
Evaluation answers whether a model is good enough, and where it fails. It ranges from automatic metrics on a fixed test set to human evaluation, where people judge outputs against a rubric for accuracy, helpfulness, safety or factual correctness.
For specialist models, automatic metrics are rarely sufficient, because being correct depends on expert knowledge the metric does not capture. Human expert evaluation with a clear rubric and multiple raters is used to produce trustworthy scores, especially for high-stakes domains.
Good evaluation is repeatable and documented: the same test set, the same rubric and recorded rater judgments, so results can be compared across model versions and shown to reviewers.
How Pathwize relates
Pathwize runs expert evaluation against your rubric, with multiple raters, recorded reasoning and time per item, so scores are defensible and every judgment is traceable to the expert who made it.
Frequently asked
Measuring how well a model does its real task, using a defined test set and criteria.
When correctness depends on judgment or expert knowledge that a simple metric cannot capture, as in medicine, law or engineering.
Use a clear rubric, more than one qualified rater, and record their judgments so results are repeatable and auditable.
Related terms