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

# Using a preset evaluator

> Use a packaged preset evaluator for a single-field trace-style input.

Use this tutorial when a packaged preset already matches your use case. This is the simplest path because the preset evaluator already defines the evaluator behavior, labeling prompt, and training prompt template.

For the purpose of this tutorial, we will use the "trace input / output only" preset evaluator, but the same principles apply to all preset evaluators.

## When to choose a preset evaluator

Choose a preset evaluator when:

* you want to use a preset evaluator that already matches your use case closely, but the current preset evaluator performance is not satisfactory
* you want to start from the packaged Luna Studio defaults

## Minimal end-to-end config

```yaml theme={null}
run_steps:
  - data_generation
  - training

pipeline_provider: "local"
metric_name: "toxicity"

data_generation:
  source_data:
    dataset:
      source_type: "huggingface"
      huggingface:
        name: "toxicity_dataset"
  output:
    dataset:
      repo_name: "toxicity-training-dataset"

training:
  dataset:
    name: "toxicity-training-dataset"
  output:
    model_name: "toxicity-model"
```

## Why this is simpler than `custom`

With a preset evaluator like `toxicity`, you usually do not need to define:

* `data_generation.metric.name`
* `data_generation.metric.type`
* `data_generation.metric.input_format`
* `data_generation.metric.class_labels`
* `training.prompt_template`

Those defaults are already provided by the packaged evaluator config.