- In short
- The five-stage eval workflow runs sequentially: define the task in specific measurable terms, build a golden dataset with labelled expected outputs, run automated code-based checks, score interpretive behaviours with a judge, then interpret and act on the results. Each stage produces an artifact that feeds the next, from a task specification through to an overall score with a per-category breakdown. A golden dataset must include labelled expected outputs, not just raw inputs, and a change that raises the mean while degrading edge-case performance does not make the system better.
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