## Draft a judge rubric tailored to an adapted dataset

`datasets.custom_evals.prepare(strdataset_id)  -> CustomEvalPrepareResponse`

**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.

### Parameters

- `dataset_id: str`

### Returns

- `class CustomEvalPrepareResponse: …`

  - `judge_prompt: str`

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

  - `adapted: bool`

    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: int`

    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

```python
import os
from adaption import Adaption

client = Adaption(
    api_key=os.environ.get("ADAPTION_API_KEY"),  # This is the default and can be omitted
)
response = client.datasets.custom_evals.prepare(
    "dataset_id",
)
print(response.judge_prompt)
```

#### Response

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