> ## Documentation Index
> Fetch the complete documentation index at: https://benchgen.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Export Datasets → Train

> Export failing benchmark cases as a labeled dataset to kick off a fine-tuning run in Train.

The most direct path from an eval failure to a model improvement is exporting the failing cases as a training dataset and routing them straight into Train.

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## When to Export

Export a dataset when:

* Your benchmark run shows a cluster of failures around a specific task or topic.
* You have enough failing cases (rule of thumb: 50+ examples) to justify a fine-tune.
* You've reviewed the failures and confirmed they're fixable by training (not a prompt or scope issue).

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## How to Export

1. Open the results report for the benchmark run you want to act on.
2. Click **Export → Send to Train**.
3. Choose which cases to include:
   * **Failing cases only** (recommended) — adds corrected labels automatically where available.
   * **All cases** — useful for building a balanced dataset.
4. Name the dataset and confirm.
5. The dataset appears immediately in **Train → Datasets**.

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## What Gets Exported

Each exported example includes:

* The original input prompt
* The model's failing response (as a negative example, if applicable)
* The expected/corrected response (as the training target)
* Metadata tags from the benchmark (topic, error type)

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## Next Steps

* [Add a dataset in Train](/train/add-a-dataset)
* [Fine-tune a model](/train/fine-tune-a-model)