---
sourceDocument: Brazil Enable AI
sourceDocumentLink: https://www.servicenow.com/docs/r/intelligent-experiences

 Release :

    - brazil

ft:locale :

    - en-US

ft:publication_title :

    - Brazil Enable AI

ft:clusterId :

    - platai

bundleId :

    - platai

workflow :

    - Platform


---

# Evaluate a prompt

# Evaluate a prompt {#ariaid-title1}

Release version: Brazil  
Updated September 10, 2026  
![](https://www.servicenow.com/docs/portal-asset/ico-clock) 2 minutes to read  
Use the AI Skill Kit evaluation tools to measure how your skill prompts perform against a dataset, using automated metrics and human feedback.

## Before you begin

Role required: sn_skill_builder.admin

## Procedure

1. Navigate to AllAI Skill KitHome.
2. Select the skill that you want to evaluate.
3. Select the Prompt performance tab.
4. Select the Evaluation runs tab.
5. Create a dataset from a table or data collection.  
   {#evaluate-prompt__table_iv4_kcz_ddc__entry__2}

   | Method | Steps |
   |-|-|
   | Create a dataset from a table | 1. Give the dataset a name and description. 2. Select Table. 3. Find the table that you want to use. 4. Select the maximum number of records that you want to use. 5. Add conditions. 6. Select Generate Preview. 7. Select the mappings. 8. Select Create. {#evaluate-prompt__ol_elt_nyv_2dc} |
   | Create a dataset from a data collection | 1. Give the dataset a name and description. 2. Select Data Collection. 3. Select a data collection that you created in AI Data Kit. 4. Select Generate Preview. 5. Select the mappings. 6. Select Create. {#evaluate-prompt__ol_htq_zyv_2dc} |
   [Table 1. Create a dataset]

   {#evaluate-prompt__table_iv4_kcz_ddc}
6. Select the add icon ![]() for Evaluation Runs.
7. Give the evaluation run a name and description.
8. Select one or more prompts that you want to evaluate.
9. Select Save \& Next.
10. Select a dataset.
11. Select Save \& Next.
12. Expand the Quality tab.
13. Select the metrics that you want to evaluate.  
    {#evaluate-prompt__table_oqq_1mv_ddc__entry__3}

    | Evaluation method | Metric | Description |
    |-|-|-|
    | Human | Human Feedback | Human evaluation is the default option available for all prompt executions that generate a response. You can rate the response with a thumbs up or thumbs down, based on your satisfaction. You also have the option to provide more detailed feedback to explain your evaluation choice. |
    | Automated | Correctness | The correctness metric assesses the generated response's accuracy, completeness, pertinence, and writing quality relative to the given instruction. This metric helps to check that the text accurately reflects the instruction, covers all important points, remains relevant, and is well written. |
    | Automated | Correctness with Golden Response | The correctness with golden response metric uses a predefined reference to assess the generated response's accuracy, completeness, pertinence, and writing quality relative to the given instruction. This metric helps to check that the text accurately reflects the instruction, covers all important points, remains relevant, and is well written. Use this metric when a reference response is available. |
    | Automated | Faithfulness | The faithfulness metric assesses whether a generated response accurately reflects the information and context provided in the given instruction. This metric helps to check that the text contains no hallucinations, fabricated facts, or unsupported conclusions, maintaining alignment with the source material. |
    [Table 2. Evaluation metrics]

    {#evaluate-prompt__table_oqq_1mv_ddc}
14. Select Save \& Next.
15. Review the evaluation choices that you made.
16. Select Save \& Evaluate.
17. **Optional:** Give a human evaluation.
    1. Select Human evaluation.
    2. Select a record to use in the evaluation.
    3. Expand the prompt and read the result.
    4. Select the thumbs up or thumbs down icon ![]() to give your evaluation.
    5. Add more information and select Submit.
    {#evaluate-prompt__substeps_xqy_st3_gdc}
**Related tasks**   

* [Create a skill](https://www.servicenow.com/docs/~ak7kL0bW_JQBd9zdU1nAA "Create a custom skill for Otto. Custom skills extend the generative AI capabilities of Otto to fit your own use cases.")
* [Create a prompt](https://www.servicenow.com/docs/aCf5iZh_zstaWO9wW1FCeQ "After you create a custom skill, create a prompt. The prompt defines the instructions that the skill sends to the LLM and the skill inputs that it uses.")
* [Use prompt assistance](https://www.servicenow.com/docs/aaqv2QUPFUCNraAA9qDGag "Use prompt assistance to start your prompt from an example in the prompt library or from a prompt that ServiceNow Otto generates.")
* [Test a prompt](https://www.servicenow.com/docs/lbl0exKhp6U2ZkGlLL33UA "After you create a prompt for your custom skill, test it before you finalize it to verify that it returns the expected results.")
* [Finalize and publish a skill](https://www.servicenow.com/docs/6QCdI481r211gzsodCxv5w "Finalize at least one prompt and publish your custom skill so that a Otto admin can activate it.")
* [Activate a skill](https://www.servicenow.com/docs/g8zwnVeP3Pt5hg4xvBIiIw "After you create and publish a custom skill, you must activate it in AI Admin Hub. Activating the skill enables you to trigger the skill within the UI.")
* [Call a custom skill from a script](https://www.servicenow.com/docs/h5PDI~msDo_PVaHTxX2Q_g "Call a custom skill from a UI action script so that you can run the skill and use its output in your instance logic.")

*[\>]: and then