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sourceDocument: Australia Enable AI
sourceDocumentLink: https://servicenow-prod.fluidtopics.net/r/pt-BR/intelligent-experiences

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ft:locale :

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ft:publication_title :

    - Australia Enable AI

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# Evaluating the prompt

# Evaluating the prompt {#ariaid-title1}

* Versão de lançamento: Australia
* 
* Atualizado 12 de mar. de 2026
* 
* ![](https://www.servicenow.com/docs/portal-asset/ico-clock) 2 min. de leitura

Evaluating the prompt is an ongoing process that occurs during and after prompt development and completion.

## Prompt evaluation overview {#evaluating-the-prompt__section_dss_msh_ccc}

To determine the effectiveness of your prompt, you should evaluate batches of test data. You should copy the model-generated responses and perform evaluations outside of Now Assist Skill Kit.

## During prompt development {#evaluating-the-prompt__section_twb_psh_ccc}

Ongoing, improvised evaluation should take place alongside the development of the prompt. This ongoing evaluation enables you to adapt the prompt based on observed model outputs. It may be tempting to test a change to a prompt
against just one or two examples, however, to avoid reacting to noise, you should look at larger batches, and consider the statistical significance of the performance differences that you observed.

## Final performance evaluation {#evaluating-the-prompt__section_y5h_zsj_ccc}

Before you deploy a skill, you should test the prompt on a representative batch of data that was isolated from the development process, that is, "test" data. You want to use isolated test data because of a phenomenon known as
prompt overfitting. Iteratively editing a prompt based on the model outputs generated on the same data that is used for testing can lead to significant over-estimates of performance. This result is because the prompt can become
overspecialized to the specific examples used in development. Even though the effect is typically less dramatic than what occurs when fitting machine learn model parameters to a test dataset, it's rooted in the same underlying
principles, and should be avoided.

## Evaluation metrics {#evaluating-the-prompt__section_khf_1tj_ccc}

Selecting the right metrics for evaluation is an important consideration. The following list provides a few approaches, each of which may be more or less appropriate depending on the use case.  
* Classification-based assessment of short generationsThis approach requires labeled records, and it works best when the labels are short, well-defined "right answers," for example, true or false, multiple-choice, or category
  selection. In these cases, the model outputs can usually be parsed and formatted, then metrics like precision, recall, F1 scores, and so on can be directly calculated.

* Assessment of longer generationsMany of the most interesting generative AI use cases require longer model generations, and there are many possible "right answers." In these cases, the output can be scored (by human
  evaluators) along several different axes, for example:

  * FaithfulnessIs the generated text faithful to the context provided in the skill prompt? (The opposite of faithfulness is hallucination, which is to say that the model injects out-of-context information.)

  * CorrectnessIs the generated text correct relative to the skill instruction?

  * HelpfulnessIs the generated text helpful relative to the task that the skill wants to accomplish? (Helpfulness is subjective but it's important to try to measure. Doing so properly requires a solid understanding of
    the needs of the people who will ultimately be using the skill.)

  * FluencyIs the generated text grammatically correct? Does it have any typos, issues with coherency, and so on?

  {#evaluating-the-prompt__ul_yx1_c5j_ccc}  
  Nota:  
  It's useful to score these properties on a scale, like 1-5, rather than with yes or no.
{#evaluating-the-prompt__ul_dsl_xtj_ccc}