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

# Labeling

> Label traces to build evaluation datasets and track quality in the Bitfab web portal

## Overview

Labeling lets you review traces and mark them with pass/fail outcomes or tags. Use it to:

* Build evaluation datasets from real production data
* Train automated graders by providing labeled examples
* Track quality trends over time
* Identify patterns in failures

## Viewing Traces

Navigate to a trace function and open the **Labeling** view to see all captured traces.

### Trace List

The list shows:

| Column             | Description                               |
| ------------------ | ----------------------------------------- |
| **Status**         | Success or error                          |
| **Duration**       | Execution time                            |
| **Grader Results** | Pass/fail outcomes from automated graders |
| **Labels**         | Human-applied labels                      |
| **Created**        | When the trace was recorded               |

### Filtering

Filter traces by:

* **Status**: Success or error
* **Date Range**: Filter by time period
* **Grader Results**: Pass or fail on specific graders
* **Labels**: Filter by human-applied labels
* **Tags**: Filter by assigned tags

### Searching

Use the search bar to find traces by:

* Input content
* Output content
* Error messages

## Trace Details

Click on a trace to view details:

### Input

The exact input passed to the function:

```json theme={null}
{
  "order_id": "order-123",
  "items": ["item1", "item2"]
}
```

### Output

The output returned:

```json theme={null}
{
  "order_id": "order-123",
  "total": 20
}
```

### Span Tree

When functions call other wrapped functions, you see a hierarchical view:

```
Trace: agent-workflow
├── Span: validate-input (guardrail)
├── Span: fetch-data (function)
└── Span: generate-response (llm)
```

Click on individual spans to see their inputs, outputs, context, and timing.

## Applying Labels

Label traces to indicate quality:

1. Open a trace
2. Review the input, output, and span tree
3. Apply a pass/fail label or add tags

Labels feed into grader training - the more labeled examples you provide, the better automated graders perform.

## Tagging

Organize traces with tags for filtering and dataset building:

### Adding Tags

1. Open a trace
2. Click **Add Tag**
3. Select an existing tag or create a new one

### Managing Tags

Navigate to **Tags** in the user menu to:

* Create new tags
* Edit tag names and colors
* Archive unused tags

## Building Evaluation Datasets

Use labeled traces to build evaluation datasets:

1. Filter to traces you want to include
2. Label traces with pass/fail outcomes
3. Apply tags to organize by feature, environment, or use case
4. Use these labeled traces to train and validate graders

## Best Practices

* **Label regularly**: Consistent labeling improves grader accuracy
* **Tag strategically**: Use tags to organize by feature, environment, or failure mode
* **Label both passes and failures**: Graders need examples of both to learn effectively
* **Start with the most impactful function**: Focus labeling effort on the trace function with the highest volume or most critical failures