## Draft a judge rubric tailored to an adapted dataset

**post** `/api/v1/datasets/{dataset_id}/custom-evals/prepare`

Takes no body: samples the dataset's own before/after pairs and rewrites the stock rubric around them, sizing the sample to what the dataset yields. Nothing is persisted and no judge tokens are spent — the returned `judge_prompt` is a suggestion to review and then POST to `/datasets/{dataset_id}/custom-evals`. Blocks for the full model round-trip; set a client timeout of several minutes.

### Path Parameters

- `dataset_id: string`

### Returns

- `judge_prompt: string`

  A ready-to-edit rubric, accepted as-is by `judge_prompt` on POST /datasets/{dataset_id}/custom-evals.

- `adapted: boolean`

  False when the stock rubric came back untouched — the sampled values did not fill both halves of the before/after contrast, or the adaptation model was unreachable. The prompt is still valid, just not tailored to this dataset.

- `sampled_rows: number`

  How many of the dataset's own examples the rubric was written against — half original, half adapted. Lower than the usual target on a thin dataset; a dataset too thin to write from at all is rejected with a 400.

### Example

```http
curl https://api.prod.adaptionlabs.ai/api/v1/datasets/$DATASET_ID/custom-evals/prepare \
    -X POST \
    -H "Authorization: Bearer $ADAPTION_API_KEY"
```

#### Response

```json
{
  "judge_prompt": "judge_prompt",
  "adapted": true,
  "sampled_rows": 0
}
```