> For the complete documentation index, see [llms.txt](https://docs.raga.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.raga.ai/ragaai-catalyst/agentic-testing/metrics/honesty.md).

# Honesty

The Honesty metric is a **span-level metric** designed to assess the accuracy and factuality of the **response** generated by the Agentic application. This metric ensures that the model's outputs are truthful and reliable, making it crucial for applications requiring high trustworthiness.

***

#### **How to Run the Metric**

1. **Access the Dataset**
   * Navigate to the dataset you wish to evaluate.
   * Click on the **Evaluate** button.
2. **Select the Metric**
   * Choose **Honesty-Alteryx** from the list of available metrics.
   * Optionally, rename the metric for clarity.
3. **Choose the Evaluation Type**
   * Select the evaluation type based on the component you want to assess:
     * **LLM**: For spans related to language model outputs.
     * **Agent**: For agent-level responses.
     * **Tool**: For outputs generated by specific tools.
4. **Define the Schema**
   * Specify the **span name** to evaluate.
   * Choose the parameter to analyse, such as:
     * `input`
     * `output`
     * `ground truth`
5. **Configure the Model**
   * Select the **model configuration** for evaluation.
6. **Set Passing Criteria**
   * Define the **pass/fail threshold** to measure the level of honesty required.
7. **Run the Metric**
   * Click on **Run** to initiate the evaluation process.

***


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