> ## 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.

# Understanding Config

> Understand the high-level structure of the run config file.

This page explains the high-level structure of the YAML run config used by Luna Studio.

> The SDK reads a single YAML file that contains both `data_generation` and `training` settings.
> You typically run one or both of these steps from the same config:
>
> * `run_data_generation(config_path=...)`
> * `run_training(config_path=...)`

## Run config structure

Your YAML file controls the overall workflow and includes top-level keys plus nested `data_generation` and `training` sections:

```yaml theme={null}
run_steps: ["data_generation", "training"]
pipeline_provider: "local"
metric_name: "custom"

data_generation: ...

training: ...
```

## Top-level fields

### `run_steps`

`run_steps` controls which parts of the workflow run.

Valid values are:

* `data_generation`
* `training`

Common patterns:

* `["data_generation", "training"]`: run the full end-to-end workflow
* `["data_generation"]`: generate or label data only
* `["training"]`: skip data generation and train from an existing labelled dataset

### `pipeline_provider`

`pipeline_provider` controls where the workflow runs.

This selects the execution backend for your run. Since you are using the SDK, you can fix this to "local".

### `metric_name`

`metric_name` selects the packaged metric config to use for the run.

Supported values include:

* `action_advancement`
* `action_completion`
* `context_adherence`
* `context_relevance`
* `prompt_injection`
* `sexism`
* `tone`
* `tool_error_rate`
* `tool_selection_quality`
* `toxicity`
* `custom`

Preset metrics provide a starting configuration for common Luna metrics. Use `custom` when your metric does not match one of the packaged presets.

Preset metric example: [Using a preset metric](/luna-studio/sdk/tutorials/preset-metric)

#### Using `custom`

Set `metric_name: "custom"` when you want to define your own metric behavior.

With `custom`, you are expected to define the metric explicitly in your config, including:

* `data_generation.metric.name`
* `data_generation.metric.description`
* `data_generation.metric.type`
* `data_generation.metric.input_format`
* `data_generation.metric.class_labels` or `data_generation.metric.llmaj_source_prompt`
* `data_generation.source_data.dataset.columns.features`
* `training.prompt_template`

The packaged `custom` template starts as a binary setup, uses `boolean` training output. You should update those values to match your use case.

Custom metric example: [Trace input / output only](/luna-studio/sdk/tutorials/trace-input-output-only)

***

## Nested config sections

### `data_generation`

The `data_generation` section controls how source data is loaded, how synthetic or labelled examples are produced, and where the resulting dataset is written.

### `training`

The `training` section controls how the model is fine-tuned, evaluated, and where training artifacts are saved.

***

## Next steps

For full field-by-field reference, continue to:

* [Data generation config](/luna-studio/sdk/how-to-train-your-luna-metric/data-generation/config/config)
* [Training config](/luna-studio/sdk/how-to-train-your-luna-metric/training/config/config)