---
title: Safety and length constraints | Adaption
description: Encode length and content safety into each run with brand_controls—specification-first data, not cosmetic post-processing.
---

Adaptive Data treats **specification** as foundational: goals such as **length** and **safety** become objectives that steer what the platform produces. **Data configuration should be structural**, not a layer of preferences applied after the fact.

In the Python SDK today, **`brand_controls`** on `datasets.run` is where you express much of that specification: target verbosity (**`length`**) and **content safety categories**. Together they help ensure adapted data matches how much your product **says** and what it is **allowed** to say.

`length` is one of: `minimal`, `concise`, `detailed`, `extensive`. `safety_categories` is a list of category names to enforce (see the OpenAPI schema and API Reference for the canonical set).

## Length

Sets how verbose adapted completions should be—aligned with UI copy limits, documentation depth, or coaching-style responses.

```
run = client.datasets.run(
    dataset_id,
    column_mapping={"prompt": "instruction", "completion": "response"},
    brand_controls={
        "length": "concise",
    },
)
```

## Safety categories

Completions that violate selected categories are handled according to platform policy—so training data stays closer to **rules you can audit**, not ad hoc filtering downstream.

```
run = client.datasets.run(
    dataset_id,
    column_mapping={"prompt": "instruction", "completion": "response"},
    brand_controls={
        "safety_categories": ["harassment", "hate"],
    },
)
```

Example values appear in tests; for production, treat the API schema as source of truth.

## Combined example

```
run = client.datasets.run(
    dataset_id,
    column_mapping={"prompt": "instruction", "completion": "response"},
    brand_controls={
        "length": "detailed",
        "safety_categories": ["harassment", "hate"],
    },
)
```

## Full script

```
import os
import time


from adaption import Adaption, DatasetTimeout


client = Adaption(api_key=os.environ["ADAPTION_API_KEY"])
dataset_id = os.environ.get("ADAPTION_DATASET_ID")


if not dataset_id:
    result = client.datasets.upload_file("training_data.csv")
    dataset_id = result.dataset_id
    while True:
        st = client.datasets.get_status(dataset_id)
        if st.row_count is not None:
            break
        time.sleep(2)


run = client.datasets.run(
    dataset_id,
    column_mapping={"prompt": "instruction", "completion": "response"},
    brand_controls={
        "length": "concise",
        "safety_categories": ["harassment", "hate"],
    },
)
print(f"Run started: {run.run_id}")


try:
    final = client.datasets.wait_for_completion(dataset_id, timeout=3600)
    print(f"Finished: {final.status}")
    if final.error:
        raise RuntimeError(final.error.message)
except DatasetTimeout:
    print("Timed out")


url = client.datasets.download(dataset_id)
print(f"Download: {url}")
```

## When to use each control

- **`length`** — Match assistant response depth to product constraints (short replies vs long explanations).
- **`safety_categories`** — Encode **policy-aligned** training data for moderated or customer-facing models.