# Evaluate (workflow)

Stage 4. **Evaluate** runs scorers and judges over your outputs to produce verdicts and scores.

## The question this stage answers

*"How good is the AI output — quantitatively?"*

## What to do

1. **Define quality dimensions.** What does "good" mean for this feature? Helpfulness? Faithfulness? Tone? Structured-output validity? List them.
2. **Pick scorers for objective dimensions.** Exact match, regex, JSON schema, semantic similarity, etc.
3. **Build judges for subjective dimensions.** One per dimension. GEPA-optimize against ≥30 labeled examples.
4. **Run.** Standard evaluation, or trace evaluation against your trace set.
5. **Read the verdict.** Top-line plus per-row failures. Spend most of your time on the failures — they're where you'll improve.

## What you'll have at the end

* A reusable scoring config (your scorers + judges)
* A baseline score
* A list of failed rows / failed traces with root-cause links

## Pre-deploy vs continuous

* **Pre-deploy** — run before merging changes; gate CI
* **Continuous** — run on production traces on a schedule; alert on drift

Both use the same scoring config; the cadence and consumer are different.

## Where to next

* [Improve →](/8.-evaluate-score-the-outputs/workflow.md)
* [Concept: Evaluations](/8.-evaluate-score-the-outputs/evaluations-1.md)
* [Concept: Judges](/8.-evaluate-score-the-outputs/judges-1.md)
* [Concept: Scorers](/8.-evaluate-score-the-outputs/scorers-1.md)
* [Tutorial: First evaluation](/8.-evaluate-score-the-outputs/01-first-evaluation.md)


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