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

# Auto-Categorization

> Use AI to automatically tag and classify your production logs based on categories you define.

Auto-categorization uses AI to automatically tag and classify your production logs based on categories you define. This adds a layer of intelligence that helps you understand usage patterns and identify trends.

## How it works

1. You define category types that align with your business needs (e.g., product areas, user intent types, issue categories)
2. For each category, you provide a clear name and description
3. As logs flow through Freeplay, the AI classifies them according to your categories
4. Categories appear in the observability dashboard for filtering and analysis

## Use cases

* **Usage analysis**: Understand what types of questions users ask most frequently
* **Issue identification**: Track which product areas generate the most problems
* **Dataset curation**: Filter logs by category to build targeted test datasets
* **Review queue creation**: Focus review efforts on specific categories

## Configuration

Auto-categorization is configured at the prompt template or agent level, similar to other evaluations:

1. Navigate to your prompt template or agent
2. Create a new evaluation with type **Multi-select**
3. Enable auto-categorization and define your categories
4. Each category needs a name (max 32 characters) and description (max 500 characters)
5. Configure whether items can be tagged with multiple categories or just one

**Best practice:** Auto-categorization works best with clear, mutually exclusive categories. If you see many items tagged as "Other" or miscategorized, refine your category descriptions.

[Learn more about auto-categorization →](/core-concepts/evaluations/auto-categorization)