---
sourceDocument: Xanadu Enable AI
sourceDocumentLink: https://servicenow-prod.fluidtopics.net/r/xanadu/intelligent-experiences

 Release :

    - xanadu

ft:locale :

    - en-US

ft:publication_title :

    - Xanadu Enable AI

ft:clusterId :

    - platai

bundleId :

    - platai

workflow :

    - Platform


---

# Build and train your model

# Build and train your model {#ariaid-title1}

* Release version: Xanadu
* 
* Updated August 1, 2024
* 
* ![](https://www.servicenow.com/docs/portal-asset/ico-clock) 3 minutes to read

After creating a model, build the model's content by adding intents, entities,
vocabulary, and test set utterances. Your NLU model content determines how the model responds to user
inputs.  
Models are made up of the following content:

* Intents: An action the user wants to do or wants the application to do.
* Entities: Object or context for an action.
* Vocabulary: Add vocabulary to help your model understand the range of words in your users' utterances.
* Test set: To assess model performance, add test utterances and the intents that you expect to be predicted for those utterances.
{#managing-model-content__ul_adv_kxb_krb}

To access the model content, navigate to NLU WorkbenchModels. The Virtual Agent tab opens by default.
Select the tab for your model's application and then select the name of the model to open the Model details
page. In the Build and train your model card, select View phase.

## Intents {#managing-model-content__section_ps1_r1c_krb}

When your model receives user input, it uses an intent to perform a system action. For example, a user types in <kbd class="ph userinput">I have a critical issue with a slow laptop</kbd>. The model matches the utterance
input to the intent #TroubleshootSlowComputer. If the intent is linked to a Virtual Agent topic, it triggers further action.

Intents contain training utterances, or examples of user inputs that would trigger the system
action. Provide realistic utterances that the model might encounter from your users. The quality
of training utterances affects the accuracy of your model.

For more information, see [NLU intents](https://servicenow-prod.fluidtopics.net/cNycXRzeIdVnj0UVXOJOBw "Intents drive your models' responses by matching a system action to user inputs. Models with good intents help Virtual Agent and Search respond to your users accurately.").

## Entities {#managing-model-content__section_ckf_y1c_krb}

Your intents use entities to provide additional context for the model when
receiving inputs. In the computer example, the laptop is the entity, or object of, the
action.

NLU entities fall into two categories: system-defined and user-defined. System entities such as DATE, TIME, and LOCATION are available by default in your instance. You can create your own user-defined
entities to provide additional associations and meaning for your business requirements.

All entities are reusable across other NLU models. However, you must add them to a training utterance for each model to use them.

For more information, see [NLU entities](https://servicenow-prod.fluidtopics.net/CYuMlU29zjUzEQAzbTiviA "Entities provide your model with additional context when receiving user input. Add entities to your utterances and intents to improve the predictions of your Natural Language Understanding (NLU) model.").

## Vocabulary {#managing-model-content__section_yxv_dbc_krb}

Your
users' input may contain a wide variety of words and phrases. Also, your
model may not understand some terms used in specialized domains or business areas.

To improve your model's ability to understand a wide range of user input, you can define
synonyms by creating vocabulary items.

For example, your model includes an entity for the term computer. When a user
types in <kbd class="ph userinput">I need a new computer</kbd>, the model knows how to respond. However,
if a user enters <kbd class="ph userinput"> <dfn class="term">laptop</dfn> </kbd> or
<kbd class="ph userinput">workstation</kbd>, the model might fail to predict the intent. You can add
vocabulary to the model to train it to understand synonyms and variations.

You can also use tables and lists as vocabulary sources. Your models can look up the vocabulary
sources when predicting intents.

For more information, see [NLU vocabulary](https://servicenow-prod.fluidtopics.net/tq_m903i7eOWq4d8GevXmQ "Use NLU vocabulary items to help the system recognize the various ways your users express their requests. Use vocabulary sources to help the system recognize objects in tables or lists, such as names of conference rooms or catalog items.").

## Test set {#managing-model-content__section_rgz_xvd_dwb}

Your model contains a default test set that you can use to evaluate the model's performance.
Initially the test set is empty, ready to be populated with your content. Add test utterances
and their expected intents to build the test set.

For more information, see [Test set creation and management](https://servicenow-prod.fluidtopics.net/y3xFPXipXEZ~RyjkoUWSRg "Use the default test set of your NLU model to test the model's performance and accuracy. Manage your test set over time by building or updating its content in the NLU Workbench.").

## Test panel {#managing-model-content__section_hg5_y1x_trb}

Access the test panel by clicking Train model or Try model in the Build and train your model phase. Training
incorporates new content into your model. With Try model, you can
manually enter individual utterances to test what intents the model predicts for them.

For more information, see [Train and try your NLU model](https://servicenow-prod.fluidtopics.net/4XbdTt0aQvTMtxtl8ueabA "Train and try your model iteratively so that its intents and entities are validated, compiled, and saved to your model.").

You can also use the test panel to provide feedback on your model's predictions. Your feedback
helps improve intent prediction. See [Test panel feedback](https://servicenow-prod.fluidtopics.net/_YwL~LM5HXjUhYj3vHmU_A "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.").

## Settings {#managing-model-content__section_n1j_xsj_krb}

Use the Settings tab to edit the name, short description, and
confidence threshold of the model. You can't change the language or purpose of the model.

For more information on the confidence threshold, see [Test and publish your model](https://servicenow-prod.fluidtopics.net/dhylMV9ceAvrFnKgnGYwIA "Assess the performance of your NLU model to identify areas for improvement. Then publish your model to make it available to other applications such as Virtual Agent.").
For more information on Settings, see [NLU model settings](https://servicenow-prod.fluidtopics.net/2rtHGKMTjAXLLzKmnstedg "Change your NLU model's name, description, or confidence threshold on the Settings page of the model overview.").
* **[NLU intents](https://servicenow-prod.fluidtopics.net/cNycXRzeIdVnj0UVXOJOBw)**   
  Intents drive your models' responses by matching a system action to user inputs. Models with good intents help Virtual Agent and Search respond to your users accurately.
* **[NLU entities](https://servicenow-prod.fluidtopics.net/CYuMlU29zjUzEQAzbTiviA)**   
  Entities provide your model with additional context when receiving user input. Add entities to your utterances and intents to improve the predictions of your Natural Language Understanding (NLU) model.
* **[NLU vocabulary](https://servicenow-prod.fluidtopics.net/tq_m903i7eOWq4d8GevXmQ)**   
  Use NLU vocabulary items to help the system recognize the various ways your users express their requests. Use vocabulary sources to help the system recognize objects in tables or lists, such as names of conference rooms or catalog items.
* **[Test set creation and management](https://servicenow-prod.fluidtopics.net/y3xFPXipXEZ~RyjkoUWSRg)**   
  Use the default test set of your NLU model to test the model's performance and accuracy. Manage your test set over time by building or updating its content in the NLU Workbench.
* **[Train and try your NLU model](https://servicenow-prod.fluidtopics.net/4XbdTt0aQvTMtxtl8ueabA)**   
  Train and try your model iteratively so that its intents and entities are validated, compiled, and saved to your model.
* **[Test panel feedback](https://servicenow-prod.fluidtopics.net/_YwL~LM5HXjUhYj3vHmU_A)**   
  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.

*[\>]: and then