> ## 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.

# Metric Output Types

> Understanding the different output types of metrics supported by Luna Studio

## Boolean

Use **Boolean** when your metric is a binary decision, such as `toxicity` or `context adherence`.

Set `metric.type: "binary"`

For binary metrics:

We recommend you add `llmaj_source_prompt` so we can auto extract the below fields, but if you wish to have granular control then:

* you must define exactly 2 class labels
* labels are typically `0` and `1`
* each class label should include a name and rubric

Example:

```yaml theme={null}
metric:
  type: "binary"
  class_labels:
    - name: "positive"
      label: 1
      rubric: "..."
    - name: "negative"
      label: 0
      rubric: "..."
```

## Categorical (multi-class)

Use **Categorical (multi-class)** when exactly one class should be assigned to each example. For example `tone`

Set:

* `metric.type: "multi-class"`

For multi-class metrics:

We recommend you add `llmaj_source_prompt` so we can auto extract the below fields, but if you wish to have granular control then:

* define one class label entry per class
* label values should be unique
* in practice, sequential integer labels are the most common pattern

Example:

```yaml theme={null}
metric:
  type: "multi-class"
  class_labels:
    - name: "neutral"
      label: 0
      rubric: "..."
    - name: "positive"
      label: 1
      rubric: "..."
    - name: "negative"
      label: 2
      rubric: "..."
```

## Not Supported

These output types are not currently supported by Luna Studio -

* Count
* Discrete
* Percentage
* Multilabel