---
title: Mitigating hallucinations | Adaption
description: Reduce fabricated content in adapted data with hallucination mitigation and web-search grounding via brand_controls.
---

Generative models can produce **hallucinations**—plausible text that is not grounded in your data or verifiable facts. When that noise lands in training or evaluation sets, it works against **durable, transferable** model behavior—the kind of control Adaptive Data is designed to reinforce.

**Hallucination mitigation** steers generations toward **verifiable** information using **web-search grounding** when enabled, so adapted completions are less likely to invent facts you will later have to filter or relabel.

Configure this per run with **`brand_controls`** on `datasets.run`. See **BrandControls** in the [API Reference](/api/index.md) for the full schema.

## Configure a run

Set `hallucination_mitigation` to `true` inside `brand_controls`:

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

You can combine this with other `brand_controls` (length, safety categories, and related options) on the same run.

## Full example

Same structure as [Getting started](/introduction/getting-started/index.md): ingest (or reuse a `dataset_id`), wait until the dataset is ready, adapt with mitigation enabled, wait for completion, then export.

```
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)


estimate = client.datasets.run(
    dataset_id,
    column_mapping={"prompt": "instruction", "completion": "response"},
    brand_controls={"hallucination_mitigation": True},
    estimate=True,
)
print(f"Estimated credits: {estimate.estimated_credits_consumed}")


run = client.datasets.run(
    dataset_id,
    column_mapping={"prompt": "instruction", "completion": "response"},
    brand_controls={"hallucination_mitigation": True},
)
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 — poll datasets.get or get_status in your environment")


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

## When to use it

Enable mitigation when outputs must stay **fact-aligned**: customer support, RAG-style workflows with external truth, compliance-sensitive text, or any pipeline where invented details are expensive to catch later. For creative or purely subjective tasks, you may omit it to save latency or credits; use `estimate=True` to compare cost before you scale.