> ## 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 Evaluators > Create human evaluator fields for manual review and annotation of AI outputs Human evaluators enable domain experts to manually assess AI outputs. Unlike Python or LLM evaluators that run automatically, human evaluators create annotation fields that team members fill in during review. **When to use human evaluators:** * Quality assessment requiring domain expertise * Edge cases that automated evaluators can't handle * Building ground truth datasets * Compliance and safety reviews ## Creating a Human Evaluator 1. Navigate to the [**Evaluators**](https://app.us.honeyhive.ai/metrics) tab 2. Click **Add Evaluator** and select **Human Evaluator** Human evaluator configuration showing name, description, return type, categories, and passing range fields ## Configuration ### Name and Description The **Description** field defines evaluation criteria for annotators. Keep it concise and actionable: ``` Rate the response quality: - Accuracy: Is the information factually correct? - Completeness: Does it fully address the question? - Clarity: Is it easy to understand? ``` Write criteria as questions annotators can answer while reviewing. Avoid lengthy rubrics - link to external guidelines if needed. ### Return Type | Return Type | Use Case | UI Component | | --------------- | ------------------------------------------ | -------------- | | **Numeric** | Ratings on a scale (1-5 stars) | Star rating | | **Binary** | Yes/no assessments | Thumbs up/down | | **Categorical** | Predefined categories with optional scores | Dropdown menu | | **Notes** | Free-form text feedback | Text input | ### Rating Scale For **Numeric** and **Categorical** types, set the scale range (e.g., 1-5). For Categorical, assign numeric values to each category to enable aggregation. ### Passing Range Define which scores indicate acceptable quality. Results outside this range are flagged for review. ## Using Human Evaluators Once created, human evaluators appear in: * **Trace detail view** - Annotate individual traces * **Review Mode** - Batch review with side-by-side input/output * **Annotation Queues** - Organized workflows for systematic review Annotation queue interface showing queued events, trace details, and human evaluator fields including Rating, Output Quality, and Comments ## Related Set up automated workflows for human review Overview of all evaluator types