---
sourceDocument: Australia Conversational Interfaces
sourceDocumentLink: https://servicenow-prod.fluidtopics.net/r/pt-BR/conversational-interfaces

 Release :

    - australia

ft:locale :

    - pt-BR

ft:publication_title :

    - Australia Conversational Interfaces

ft:clusterId :

    - convint

bundleId :

    - convint

workflow :

    - Platform


---

# Legacy - Modify models

# Legacy - Modify models {#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

Test and modify the Virtual Agent models so they more accurately
predict user intents.

## Antes de Iniciar

Importante:  
Conversational Analytics dashboard is being prepared for future deprecation. It will be supported until deprecation but will no longer be available for installation. A new Conversational Analytics dashboard in Platform Analytics experience, which meets the compliance requirements of Government Community Cloud (GCC), and thus FedRAMP authorized, is available. See [Conversational Analytics dashboard in Platform Analytics experience](https://servicenow-prod.fluidtopics.net/3ta4qak9UP0IQHIangtqmQ "The Conversational Analytics dashboard in Platform Analytics experience helps you improve Virtual Agent (VA) interactions with users by providing insights into conversational data. The dashboard helps you refine topics and improve the deflection rate of VA.").

For details on the deprecation process, see the [Deprecation Process \[KB0867184\]](https://support.servicenow.com/kb_view.do?sysparm_article=KB0867184) article in the Now Support Knowledge Base.

If you are an existing user of this dashboard and want to migrate analytics data to the new dashboard, see [Migrate data to Conversational Analytics dashboard in Platform Analytics experience \[KB1651556\]](https://support.servicenow.com/kb?id=kb_article_view&sysparm_article=KB1651556).

Role required: Chat Analytics Admin

## Por Que e Quando Desempenhar Esta Tarefa

Many ServiceNow products, such as ITSM, HRSD, and CSM include NLU models for Virtual Agent (VA) topics. The NLU plugin contains entities that are common across all products. You can modify those models. To learn how to train and test the models used for VA conversations, see [Natural Language Understanding](https://www.servicenow.com/docs/access?context=nlu-landing&version=australia&pubname=australia-intelligent-experiences&ft:locale=en-US).

## Procedimento

1. Navigate to AllConversational AnalyticsDashboard and select the NLU Prediction tab.
2. Select View NLU Workbench View more .  
   The Models page opens that displays the models that predict intents in your
   setup.


3. **Opcional:** If none of the models contain intents and utterances you'd like in your setup, select Create Model and follow the instructions in [Create an NLU
   model](https://www.servicenow.com/docs/access?context=create-nlu-modelx&version=australia&pubname=australia-intelligent-experiences&ft:locale=en-US).
4. Click a model to display the intents the model predicts.  
5. **Opcional:** Click an intent to display the utterances associated with it.  
6. **Opcional:** To create a new intent and add it to the model:
   1. Select New Intent.  
      The Create a new intent dialog box opens.
   2. Enter the name of the new intent in Intent Name, a description of it in the Description field, and select Save.  
      A page for your new intent opens.

   3. Enter an utterance for your new intent and select Add.  
      You can repeat this step multiple times to add multiple utterances.
   {#modify-model__substeps_km2_jtk_g4b}
7. **Opcional:** Import one or more intents by selecting Import Intents.  
   The Import Intents dialog box opens, which displays the intents you can import.
   1. Find the intent(s) to import by entering a search term, or by clicking an arrowhead to display the intents in a folder, and then selecting one or more check boxes.
   2. Select Import.
   {#modify-model__ol_nsr_cpd_kyb}
8. **Opcional:** If you added or imported intents:
   1. Select Train to add the intents to the model.
   2. Select Train to train the model.  
      If successful, a banner at the top reads, The model has been successfully trained.
   {#modify-model__substeps_cbp_cwk_g4b}
9. To test all the utterances, select Test and enter a sentence a user might enter in a chat to see if NLU understands the intent of your sentence.  
10. If the models fail to predict the intent, add additional intents to the model, or utterances to the intents, as described in step 6.