> For the complete documentation index, see [llms.txt](https://docs.layerlens.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.layerlens.ai/9.-improve-tune-the-system/judge-optimization.md).

# Judge Optimization (GEPA)

GEPA judge optimization — automatically tune your judge rubric to match labeled ground truth.

{% hint style="info" %}
**Available in Stratix Premium.** This surface is part of the logged-in workspace at [stratix.layerlens.ai](https://stratix.layerlens.ai). Stratix Public users can browse the catalog but cannot use this feature.
{% endhint %}

GEPA optimization tunes a judge's rubric prompt against a labeled ground-truth set. The result is a rubric that better matches your team's actual quality bar — typically a 10-20 percentage-point lift in human-agreement rate.

## What GEPA does

GEPA explores prompt variations, runs each against your labeled examples, and picks the variation with the highest agreement rate.

## Prerequisites

* A built judge (see [Judges](/8.-evaluate-score-the-outputs/judges.md))
* ≥30 labeled examples — input/output pairs with the human verdict you'd want the judge to produce
* ECU credits to run the optimization

## Running GEPA

### From the UI

1. Open the judge in **Premium → Agent Evaluation → Judges**
2. Click **Optimize**
3. Upload or select your labeled examples
4. Configure: number of iterations, target metric (agreement, F1, accuracy)
5. Run

### From the SDK

```python
from layerlens import Stratix
client = Stratix()

opt = client.judge_optimizations.create(
 judge_id="judge_abc",
 labeled_examples_dataset_id="dataset_xyz",
 iterations=20,
)
result = client.judge_optimizations.wait_for_completion(opt.id)
print(f"Agreement before: {result.before}, after: {result.after}")
```

## Reading the result

The optimization result shows:

* **Agreement rate before** — how often the original rubric matched your labels
* **Agreement rate after** — how often the optimized rubric matches
* **Diff** — what changed in the rubric prompt
* **Per-iteration history** — the trajectory of the optimization

## When to re-optimize

* When you add more labeled examples
* When your team's quality bar shifts (you label things differently than before)
* When the judging model is upgraded

## How GEPA fits in agentic evaluation

For agentic evaluations, GEPA-optimized judges keep your subjective bar honest. Out-of-the-box judges drift; tuned judges hold tight to your labels.

## Where to next

* [Concept: Judges](/8.-evaluate-score-the-outputs/judges-1.md)
* [Tutorial: Optimize a judge with GEPA](/9.-improve-tune-the-system/05-gepa-optimize.md)
* [Judges](/8.-evaluate-score-the-outputs/judges.md)