---
sourceDocument: Yokohama Enable AI
sourceDocumentLink: https://servicenow-prod.fluidtopics.net/r/yokohama/intelligent-experiences
Release :
- yokohama
ft:locale :
- en-US
ft:publication_title :
- Yokohama Enable AI
ft:clusterId :
- platai
bundleId :
- platai
workflow :
- Platform
---
# Build and train your model
# Build and train your model {#ariaid-title1}
Release version: Yokohama
Updated January 30, 2025
 3 minutes to read
Summarize
 Summarized using AI
This content was generated using new OpenAI-powered functionality. Results are provided on an as is basis and are not guaranteed to be accurate or complete.
## Summary of Build and train your model
This guide explains how to build and train a Natural Language Understanding (NLU) model in ServiceNow by adding key components such as intents, entities, vocabulary, and test utterances.
These elements define how your model interprets user inputs and triggers system actions, enabling more accurate and context-aware responses in Virtual Agent and Search applications.
Show full answer Show less
## Key Features
* **Intents:** Represent the user's desired action. Training utterances---examples of user inputs---are added to improve intent detection accuracy. Linking intents to Virtual Agent topics enables automated responses and workflows.
* **Entities:** Provide additional context within intents by identifying objects or parameters in user inputs. System-defined entities like DATE or LOCATION are available by default, and you can create user-defined entities tailored to your business needs. Entities must be included in training utterances for each model that uses them.
* **Vocabulary:** Enhances the model's understanding of synonyms and domain-specific terms by allowing you to define alternate words or phrases. Vocabulary sources such as tables or lists enable the model to recognize diverse expressions, improving intent prediction.
* **Test Set:** Contains utterances paired with expected intents to evaluate model performance. You can build and manage this set over time to ensure ongoing accuracy.
* **Test Panel:** Allows you to train the model with new content and manually test utterances to verify intent predictions. Feedback functionality helps refine the model by correcting mispredictions.
* **Settings:** You can configure basic model details such as name, description, and confidence threshold. Language and purpose settings are fixed.
## Practical Application for ServiceNow Customers
By following these steps, you can create an NLU model that accurately interprets user requests in your ServiceNow environment. Properly defined intents and entities ensure that Virtual Agent and Search features respond correctly, improving user satisfaction and operational efficiency. Using vocabulary and test sets helps your model handle diverse language and maintain accuracy as your application evolves. The test panel supports iterative improvements by allowing real-time testing and feedback integration.
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 I have a critical issue with a slow laptop. 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/iWNsKbLq1ezyjD2AmnAEAQ "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/8_kRugAhXu0dvrQlyMV90A "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 I need a new computer, the model knows how to respond. However,
if a user enters laptop or
workstation, 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/40o~O6msNR2pVxuwEcHxMQ "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/wP~RQuZhfFfx16bQPE~ccA "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/KRneHtgkkSp2DpurFaQdXw "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/UsTXwtfz5eAl9GoBQWeJFw "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/DPaBLeE6TiKefL~QJbRX8Q "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/9DCue~pqIqM_bEihwOdQIQ "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/iWNsKbLq1ezyjD2AmnAEAQ)**
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/8_kRugAhXu0dvrQlyMV90A)**
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/40o~O6msNR2pVxuwEcHxMQ)**
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/wP~RQuZhfFfx16bQPE~ccA)**
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/KRneHtgkkSp2DpurFaQdXw)**
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/UsTXwtfz5eAl9GoBQWeJFw)**
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