> 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/more-in-this-section-9/judge-optimizations-1.md).

# Judge optimizations

Automated judge optimization tunes a judge's prompt against examples to improve scoring accuracy. Use the `client.judge_optimizations` resource.

## Estimate cost

Always estimate before running:

```python
estimate = client.judge_optimizations.estimate(
 judge_id="judge_abc",
 budget="medium", # "light" | "medium" (default) | "heavy"
)
```

## Create (start optimization run)

```python
optimization = client.judge_optimizations.create(
 judge_id="judge_abc",
 budget="medium",
)
# optimization.status starts as "pending" / "in_progress"
```

## Poll

```python
import time
while True:
 optimization = client.judge_optimizations.get(optimization.id)
 if optimization.status.value in ("success", "failure"):
 break
 time.sleep(30)
```

## Apply (commits optimized prompt)

```python
if optimization.status.value == "success":
 print(f"Baseline: {optimization.baseline_accuracy}")
 print(f"Optimized: {optimization.optimized_accuracy}")
 client.judge_optimizations.apply(optimization.id)
 # Creates a new judge version with the optimized goal
```

## List

```python
response = client.judge_optimizations.get_many(judge_id="judge_abc", page=1, page_size=20)
```

## Object fields

`JudgeOptimizationRun` includes:

* `status` — `OptimizationRunStatus` enum (`pending`, `in_progress`, `success`, `failure`)
* `baseline_accuracy` — measured before optimization
* `optimized_accuracy` — measured after
* `original_goal` — original judge `evaluation_goal`
* `optimized_goal` — new prompt
* `estimated_cost` / `actual_cost`
* `applied_version` — judge version ID once applied

## Budget levels

| Budget             | When to use                                         |
| ------------------ | --------------------------------------------------- |
| `light`            | Quick validation, ≤30 labeled examples              |
| `medium` (default) | Standard production tuning, 30-100 labels           |
| `heavy`            | Maximum exploration, 100+ labels, high-value judges |

## See also

* [Tutorial 5: GEPA optimize](/9.-improve-tune-the-system/05-gepa-optimize.md)
* [Concept: Judges](/more-in-this-section-9/judges-2.md)
* [Bootstrap a judge before GEPA](/more-in-this-section-6/bootstrap-judges.md)
* [SDK sample: `judge_optimization.py`](https://github.com/layerlens/stratix-python/blob/main/samples/core/judge_optimization.py)


---

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