> ## 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.

# Human Annotation

> Technical documentation for creating custom human evaluator fields in HoneyHive

Human annotation allows for manual review and evaluation of AI outputs by human reviewers.

## Creating a Human Evaluator

1. Navigate to the [**Evaluators**](https://app.us.honeyhive.ai/metrics) tab in the HoneyHive console.
2. Click `Add Evaluator` and select `Human Evaluator`.

<Frame>
  <img src="https://mintcdn.com/honeyhiveai/76aQ7hOOlqHYtp6r/images/product-categorical.png?fit=max&auto=format&n=76aQ7hOOlqHYtp6r&q=85&s=2c3ca0e45f140da8452cf35820b316ed" alt="humaneval" width="3024" height="1568" data-path="images/product-categorical.png" />
</Frame>

## Evaluation Criteria

Define clear evaluation criteria for annotators in the `Description` field:

```markdown theme={null}
1. Relevance: Is the response directly related to the prompt without unnecessary details?
2. Clarity: Is the message clear and easily understandable?
3. Word Economy: Are unnecessary words, phrases, or sentences eliminated?
4. Precision: Does the response use precise language without being vague?
5. Elimination of Filler: Are redundant or filler words removed?
6. Logical Flow: Does the response follow a logical sequence without unnecessary jumps?
7. Brevity vs. Completeness: Is the response concise while still covering all necessary points?
8. Consistency: Does the response maintain consistent conciseness throughout?
9. Engagement: Does the response keep the reader's interest despite its brevity?
10. Overall Impact: Does the response effectively convey the message concisely?
```

## Configuration

### Return Type

Options:

1. `Numeric`: For ratings on a scale
2. `Binary`: For yes/no evaluations
3. `Notes`: For free-form text feedback
4. `Categorical`: For categorization tasks

### Rating Scale

For `numeric` return type and, in some cases, `categorical` return type (i.e where numerical labels are defined), specify the scale (e.g., 1-5).

### Passing Range

Define the range of scores considered acceptable.

## In-App Annotation

Once created, human evaluators are available throughout the UI - in traces, `Review Mode` or `Annotation Queues`. You can invite domain experts to annotate traces in any project.

<Frame type="glass">
  <img src="https://mintcdn.com/honeyhiveai/81DpusKRfAED9ab1/images/AnnotationQueues.png?fit=max&auto=format&n=81DpusKRfAED9ab1&q=85&s=69951687bdb69d9874f5fe4c6256aed4" alt="humaneval" width="3024" height="1562" data-path="images/AnnotationQueues.png" />
</Frame>
