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

# Overview

> Generate a labeled training dataset for your metric.

Data generation produces / labels the training dataset for your Luna metric.

You run it using:

```python theme={null}
from galileo_luna_ft.data_generation import run_data_generation

training_dataset_path = run_data_generation(config_path="./config.yaml")
```

## Inputs Required

1. Config
2. Dataset
3. LLM provider integration

## What it does

1. Reads your `data_generation` config section
2. Loads the source dataset (CSV or Hugging Face)
3. Consumes a small portion of your test set to create synthetic training data (defaults to 20%, can be configured with `source_data.sampling.enhancement_fraction`)
4. Generates synthetic examples using your configured LLM
5. Writes a dataset artifact locally and/or pushes to Hugging Face (depending on `output.push_to_hub`)

## Key concepts

* **Metric type**: `binary` or `multi-class` ([Read More](/luna-studio/sdk/how-to-train-your-luna-metric/data-generation/config/metric-output-types))
* **Input format**: `single`, `tuple`, or `rag` ([Read More](/luna-studio/sdk/how-to-train-your-luna-metric/data-generation/config/metric-input-types))

## Output Dataset

The output dataset here has 2 splits -

* Train: The training data for the Luna metric
* Test: Your original test set minus the consumed portion (defaults to 80% of your original test set)

The output dataset is saved as a Huggingface formatted dataset.

Next: see the detailed [Config file](/luna-studio/sdk/how-to-train-your-luna-metric/data-generation/config/config).