---
title: Reasoning traces | Adaption
description: Add explicit reasoning steps to adapted completions with recipe_specification.recipes.reasoning_traces.
---

**Reasoning traces** are part of Adaptive Data’s **recipe library**—alongside options such as deduplication and prompt rephrasing—so you can shape *how* models reason over your data, not only the final string.

Traces expose an intermediate reasoning path with each adapted completion. That supports **auditability** (you can inspect *how* an answer was derived), **distillation**, and supervised fine-tuning that teaches models to show structured reasoning before you strip traces for production.

In the SDK this is a **recipe** toggle under `recipe_specification`, not a `brand_controls` field.

Exact output shape (how the trace relates to the completion) depends on the API and recipe version. Inspect downloaded results and the [API Reference](/api/index.md) for `RecipeSpecification` and response types.

## Configure a run

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

## Full example

```
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"},
    recipe_specification={"recipes": {"reasoning_traces": True}},
)
print(f"Run started: {run.run_id}, ~{run.estimated_credits_consumed} credits")


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 — check status manually")


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

## Combining with other recipes

`RecipeSpecificationRecipes` includes additional toggles (for example `deduplication`, `preference_pairs`, `prompt_rephrase`). Set multiple keys under `recipes` in one run; omitted keys keep backend defaults.

## When to use it

Use reasoning traces when **interpretable intermediate steps** matter: debugging adaptation quality, building chain-of-thought–style training data, or meeting internal review requirements. Omit them when you only need short final answers and want to minimize tokens and cost.