NLU intents

  • Release version: Yokohama
  • Updated January 30, 2025
  • 3 minutes to read
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    Summary of NLU intents

    NLU intents are essential components that enable your Natural Language Understanding (NLU) models to interpret user inputs and trigger corresponding system actions. They serve as the core mechanism for translating user utterances into actionable tasks within ServiceNow, improving the accuracy of Virtual Agent and Search responses. Properly designed intents, supported by relevant utterances, vocabulary, and entities, enhance model performance and user experience.

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    Key Features

    • Intents and Actions: Each intent corresponds to a system action, such as creating service requests or managing users, allowing models to handle a wide range of user requests.
    • Model Capacity: Models can contain up to 750 intents and 20,000 utterances, but performance may degrade when exceeding 300 intents or 4,500 utterances due to longer training and testing times.
    • Utterances: Intents include training utterances that help the model recognize user inputs. Each intent requires at least 5 utterances for effective training, and utterances are limited to 25 words or 200 characters.
    • Vocabulary Integration: Incorporating organization-specific vocabulary enhances intent prediction accuracy by aligning the model with domain-specific language.
    • Entities: Entities add context and precision to utterances, improving the model’s ability to perform intended actions.
    • Intent Management: Intents can be enabled or disabled, though intents linked to published Virtual Agent topics cannot be deactivated or deleted. Changes require retraining the model to take effect.
    • Issue Detection and Resolution: The system identifies conflicts or overlaps between intents, such as duplicated utterances, and provides tools to resolve these issues to maintain model accuracy.
    • Reusability: Intents from prebuilt NLU models can be imported and reused across models within the same application scope, streamlining model development.

    Practical Guidance for ServiceNow Customers

    • When building NLU models, aim to create at least five intents before extensive testing to reduce unexpected intent interactions.
    • Maintain a balanced number of intents and utterances to optimize training time and model responsiveness.
    • Regularly review and resolve intent conflicts using the provided issue cards to ensure clear intent differentiation.
    • Leverage vocabulary and entities to tailor the model to your organizational language and context, enhancing prediction precision.
    • Retrain your model after enabling or disabling intents to update its predictive capabilities.
    • Utilize intent reuse features to accelerate model creation by importing existing intents from prebuilt models.

    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.

    Think of intents as the core that drives the comprehension of natural language. Intents help the models translate utterances from your users into actions that the system can perform. Intents perform best if your utterances, entities, and vocabulary work together to support your model. Using vocabulary and realistic utterance examples can help the system to be more accurate when predicting intents.

    When creating your Natural Language Understanding (NLU) model, you add intents to match user inputs with system actions. The more intents that a model has, the more actions it can take when it receives utterances from your users. Model intents can perform various actions, from creating hardware service requests to adding users to groups.

    To access your model's intents, navigate to a model's overview page. In the Build and train your model phase, select View phase. The Intents tab shows by default.

    Intents tab of the Build and train your model phase

    A model can contain up to 750 intents. However, models with over 300 intents or 4500 utterances, whichever comes first, take longer to train, test, and publish.

    Note:
    As you create intents, keep in mind that they can sometimes impact each other. For example, you build and test an intent that works on one or two utterances. But when you test it in a larger intent environment, it might behave differently. To reduce the likelihood of such an event, you may want to create at least five intents in a model before you start proper testing.

    To start adding content to your model, see Create an NLU intent.

    The Enabled column shows whether or not the intent is active in predictions. An NLU admin can deactivate an individual intent but keep it in the model.

    However, if an intent is mapped to a published Virtual Agent topic:

    • You cannot deactivate the intent.
    • You cannot delete the intent.
    After changing the Enabled status of an intent, retrain the model.

    Utterances

    Intents contain training utterances, which are examples of inputs that the model may see from your users. Each intent in a model has its own utterances. When trained, the model learns to recognize similar utterances from your users and then respond with the matching intent.

    Select an intent to access the intent details page. The Utterances tab lists all the utterances currently in the intent.

    Utterances tab for an example intent.

    After adding utterances, you can edit, copy, move, or delete the utterances using the icons in the right column. You can move or delete multiple utterances by selecting the box on the left first and using the Perform action on selected rows button.

    Here are some things to consider when adding utterances to intents:
    • A model must have at least 1 intent, with a minimum of 5 utterances in each intent.
    • An intent needs at least 5 utterances to begin training.
    • The system currently supports utterances up to 25 words or 200 characters. Utterances that exceed that limit fail to return an intent prediction.
    • The system currently supports up to 20,000 utterances in a single model.
    • Models with more than 4,500 utterances take longer to train, test, and publish.

    Provide vocabulary for any words or phrases that are relevant to your organization or domain when adding utterances to your model. The vocabulary helps with intent prediction for words and phrases that your users are likely to use. You can use the @ symbol when adding an utterance to call on a vocabulary source. For more context and examples, see NLU vocabulary.

    Associated entities

    Your model uses entities to provide additional context and meaning when predicting user input. You add entities to the training utterances of your intent to provide the system with more information to perform the intended action.

    The Associated entities tab for an example intent.

    For more information, see NLU entities.

    Intent issues

    Building large models increases the chance that intents overlap, conflict, or fail to contain enough training utterances. For example, the utterance examples in one intent may end up identical to the examples in another intent. If your intents conflict, the model may not know which intent to predict when receiving user input.

    If your model has issues or conflicts, the Intents page displays cards showing the number of intents affected. In the Intents tab of the Build and train your model phase, the issue cards are highlighted.

    Select a card to see a filtered list of intents with that issue. Resolving issues ensures that your intents meet the requirements and work as intended.

    For more information, see Resolve intent issues.