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

# How do I use Atla in a dev workflow?

> Using Atla for development iteration and testing, not just production monitoring

Atla isn't just for monitoring production traces and shipping improvements faster. It's also used to help teams iterate so they can launch in days, not weeks.

## Development Workflow Steps

<Steps>
  <Step title="Create custom metrics">
    Navigate to your Metrics screen and create custom metrics that matter to your domain. While we automatically flag error types and failure patterns, you can add domain-specific metrics like `tool_call_efficiency`.
  </Step>

  <Step title="Configure metadata for test runs">
    Set up metadata to track different test configurations. For example, you might run three versions of a prompt that are concise, balanced, and verbose:

    ```python theme={null}
    from atla_insights import configure, set_metadata

    # Configure global metadata for the experiment
    configure(
        token="<YOUR_TOKEN>",
        metadata={"experiment": "prompt_comparison"}
    )

    # Then set dynamic metadata for each test run
    set_metadata({"prompt": "v1"})  # concise version
    set_metadata({"prompt": "v2"})  # balanced version  
    set_metadata({"prompt": "v3"})  # verbose version
    ```
  </Step>

  <Step title="Run your tests">
    Execute your test suite with the configured metadata and custom metrics enabled.
  </Step>

  <Step title="Compare results">
    Navigate to the **Compare** screen to analyze the relative error rates and performance on your custom metrics across different configurations.
  </Step>

  <Step title="Deep dive into issues">
    Click "View" on any column to deep dive into the specific step-level errors in the traces to understand deeper issues.
  </Step>

  <Step title="Continue iterating">
    Continue to iterate on different dimensions. Use the "experiment" metadata tag to track performance of different experiments, such as testing different architectures and setups.
  </Step>
</Steps>

## Example Metadata Configuration

```python theme={null}
from atla_insights import configure, set_metadata

# Configure with global experiment metadata
configure(
    token="<YOUR_TOKEN>",
    metadata={"experiment": "model_comparison"}
)

# Set dynamic metadata during runtime
set_metadata({"prompt": "v1", "model": "gpt-4"})
set_metadata({"prompt": "v2", "model": "claude-3"})
```

This approach helps you systematically test and improve your AI applications before they reach production.