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

# Annotation Queues

> Learn how to create and manage annotation queues for human review and labeling.

Annotation queues help you organize events for human review and quality assessment. Use them when subjective qualities like brand voice, creative quality, or domain-specific accuracy are best assessed by humans rather than automated evaluators.

<Note>
  **Before you start:** Create [Human Evaluators](/v2/evaluators/human) to define what fields your team will annotate (quality ratings, categories, feedback notes, etc.). Annotation queues organize **which** events to review; human evaluators define **what** to assess.
</Note>

<Frame>
  <img src="https://mintcdn.com/honeyhiveai/81DpusKRfAED9ab1/images/AnnotationQueues.png?fit=max&auto=format&n=81DpusKRfAED9ab1&q=85&s=69951687bdb69d9874f5fe4c6256aed4" alt="Annotation queue interface showing queued events and human evaluator fields" width="3024" height="1562" data-path="images/AnnotationQueues.png" />
</Frame>

## When to Use Annotation Queues

Annotation queues are particularly useful for:

* **Quality Assurance** - Route low-confidence predictions or edge cases for human review
* **Active Learning** - Identify and label examples where your model is uncertain
* **Compliance Review** - Flag sensitive or regulated content for manual verification
* **Training Data Curation** - Collect and label examples to improve your datasets
* **Performance Monitoring** - Sample production traffic for ongoing quality assessment

## Creating Annotation Queues

Choose your approach based on your workflow:

| Create Method        | When to Use                                                                 |
| -------------------- | --------------------------------------------------------------------------- |
| **Manual Selection** | One-time review of specific edge cases or issues you've already identified  |
| **Automated Rules**  | Continuous quality monitoring with automatic sampling of production traffic |

### Manual Selection

Create a queue from specific events you've already identified. The Traces page provides three views for finding events: Sessions (complete traces), Completions (individual LLM calls), or All Events (every span).

1. Navigate to **Traces** in your project
2. Apply filters to identify the events you want to review
3. Select the events you want to include (or select all matching events)
4. Click the **Add to** dropdown menu
5. Select **Add to Queue**

<Frame>
  <img src="https://mintcdn.com/honeyhiveai/VY6XDocpo3PwSrYR/images/product-queue-creation.png?fit=max&auto=format&n=VY6XDocpo3PwSrYR&q=85&s=b3ecd43a90eaa3bd31b96b9491140474" alt="Traces page with selected events and Add To dropdown showing Add to Queue option" width="3024" height="1562" data-path="images/product-queue-creation.png" />
</Frame>

This approach is useful when you've identified specific events that need immediate attention.

### Automated Rules

Set up a queue that continuously captures matching events as they arrive:

**Option 1: From Traces**

1. Follow the manual selection steps above
2. After applying your filters, toggle the **Queue automation** checkbox when creating the queue
3. Your filters will be saved as automation rules

**Option 2: From Annotations Tab**

1. Navigate to the **Annotations** tab in your project
2. Click **Create Queue**
3. Set up your filter criteria to define which events should be automatically added
4. Toggle the **Queue automation** checkbox
5. Save your queue configuration

**How automation works:**

* New events matching your filters are automatically added in real-time
* You can edit filters and automation settings anytime by clicking on the queue
* Disable automation to pause capturing events without deleting the queue

### Common Filter Criteria

Filters determine which events are added to your queue. Common criteria include:

* **Event type** - Sessions, completions, tool calls, chains
* **Evaluator scores** - `metrics.accuracy < 0.7` or `metrics.toxicity > 0.5`
* **Metadata fields** - `metadata.environment = "production"`, `metadata.user_tier = "enterprise"`
* **User feedback** - `feedback.rating < 3` or `feedback.helpful = false`
* **Date ranges** - Last 7 days, specific time windows
* **Performance** - `duration > 5000`, `cost > 0.10`

For complete filter syntax and available operators, see [Query Trace Data](/v2/tracing/query-data#available-filter-operators).

## Reviewing and Annotating Events

Once events are in a queue:

1. Navigate to the **Annotations** tab in your project
2. Click on a queue to open Review Mode
3. For each event, view the inputs and outputs, then fill in the annotation fields defined by your human evaluators
4. Navigate between events:
   * Right arrow (`→`) for next event
   * Left arrow (`←`) for previous event
   * `Enter` to save current annotations and advance

## Managing Queues

### Editing Queue Settings

Click on a queue to:

* Update the queue name and description
* Modify filter criteria (affects future auto-additions)
* Enable or disable automation

## Related

<CardGroup cols={2}>
  <Card title="Human Evaluators" icon="user-check" href="/v2/evaluators/human">
    Create annotation fields before setting up queues
  </Card>

  <Card title="Query Trace Data" icon="filter" href="/v2/tracing/query-data">
    Learn about available filters for queue automation
  </Card>

  <Card title="Explore in UI" icon="eye" href="/v2/tracing/ui-flows">
    Navigate Traces to select events manually
  </Card>

  <Card title="Experiments Framework" icon="flask" href="/v2/evaluation/concepts">
    Understand how human evaluation fits in the evaluation workflow
  </Card>
</CardGroup>
