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

 Release :

    - australia

ft:locale :

    - pt-BR

ft:publication_title :

    - Australia Enable AI

ft:clusterId :

    - platai

bundleId :

    - platai

workflow :

    - Platform


---

# Test panel feedback

# Test panel feedback {#ariaid-title1}

* Versão de lançamento: Australia
* 
* Atualizado 12 de mar. de 2026
* 
* ![](https://www.servicenow.com/docs/portal-asset/ico-clock) 4 min. de leitura

When testing your NLU model on the Try model section of the
test panel, use this feature to provide feedback on the model's intent predictions.

## Summary context {#test-model-panel-feedback__section_sqy_1mk_brb}

When a model is trained and tested for an utterance and the model returns an intent
prediction, you can provide a thumbs up or thumbs down rating on the predicted intent it
returns. Marking a different intent prediction as correct adds the utterance to the
corrected intent. All other feedback is captured for continual learning. The system then
incorporates your feedback to optimize the model predictions. This feature requires the
nlu_admin role to access and test the model. NLU editors can also
access the test panel if an NLU admin assigns them to it.

## Providing prediction feedback {#test-model-panel-feedback__section_jsg_cnk_brb}

The ratings you
provide help the system to match an intent to an utterance. These ratings are essential for
the system to continuously learn, evolve, and improve the accuracy of the intent predictions
based on user input. They also enable you to notify the system if the intent prediction is
correct or not.  
The following scenarios below show examples of how to interact with your model test panel and provide prediction feedback to the system. In all scenarios, you use these four steps:

1. In the Build and train your model phase of your model, select Try model to open the test panel.
2. In the test panel's Enter an utterance to test field, enter a brief utterance that's similar to a training utterance in one of the intents.
3. Click GO.

   Result: The system returns its
   predictions for your test utterance in the Top Predictions(s)
   section of the test panel.
4. Click the Thumbs Up icon or the Thumbs Down icon.

   If you want the system to know it has predicted the correct
   intent for your utterance, select the Thumbs Up icon.

   In
   all other cases, select the Thumbs Down icon, which opens the
   Provide feedback to improve this prediction section. Here you
   can choose an intent other than the top predicted intent.
{#test-model-panel-feedback__ol_q1t_5wj_krb}

Scenario 1: On the Try model
section of the test panel, you enter <kbd class="ph userinput">help with hr</kbd> as the utterance.
When the top prediction results appear, you're confident that the predicted intent is the
correct match to your utterance. So in this case, you click the Thumbs Up icon.  
Results:

* The system predicted the correct intent, which in this case is #CreateHRGeneralInquiryCase.
* Your feedback notifies the system that it has matched the correct intent to your test utterance.
{#test-model-panel-feedback__ul_w55_bwf_4rb}

Scenario 2: In a
separate model on a separate instance, a different user enters the same <kbd class="ph userinput">help with hr</kbd> utterance. The system responds with the top prediction results for the
intent, but the user isn't sure if it's the correct intent or not. So this user clicks the
Thumbs Down icon, as shown in the image below.

Result: The panel expands to show the
Provide feedback to improve this prediction section where users can
submit feedback that may help to improve the intent prediction.  
There are two options here:

* If users click the Its correct intent should be: button, a list appears where they can choose a more appropriate intent for the test utterance. In this example scenario, a user selects the Retrieve Work Location intent, as shown in the image below.

* If you click the I'm not sure what the correct intent is prompt, instead of returning a top prediction, the system shows the next best intent predictions available.
{#test-model-panel-feedback__ul_cfw_r21_mrb}

Scenario 3: In a separate model on a separate instance,
another user submits an utterance that uses gibberish, or uses a language that's different
from the language the model uses. For example, a user mistakenly submits an utterance
comprised of both non-English and English languages, as shown in the image
below.

Result: The system doesn't return a prediction because the
utterance uses two different languages together. Since no intent was predicted, the user
clicks the Give feedback option which expands the Try model section
to show other intent alternatives.

So instead of choosing an intent from the prompt, this user selects the No intent should be predicted option.  
Nota:  
When you choose and save No intent should be predicted, the utterance is removed from all intents which it is a part of.

Scenario 4: Along with choosing from a list of
your model's intents for a prediction, you can also directly notify the system that the
utterance is irrelevant to the model. To do this, you click the Exclude this model's predictions for this utterance button, then click Save changes.

Result: A banner appears at the top of the screen confirming the
user feedback for the prediction is saved, as shown in the image below.

## Accessing your feedback records {#test-model-panel-feedback__section_itt_vtb_krb}

Your feedback data is stored in the ml_labeled_data table, which is
also used by other ServiceNow products. This table can also house
multiple sources, such as Virtual Agent chat logs that can be used for future
predictions.