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The thresholds feature provides configurable score boundaries that determine how the NLU model interprets and classifies user inputs based on their intent. This feature enables precise control over intent recognition, ensuring the NLU can confidently identify intents, prompt for reconfirmation when needed, or reject unclear inputs.

Key benefits

  • Improved accuracy. Adjustable thresholds enhance the reliability of intent mapping by setting clear confidence levels.
  • Flexible control. You can fine-tune the system’s sensitivity to match specific use cases or performance needs.

Prerequisites

Before using thresholds, ensure the following:
  • You have an active flow in the project.
  • You’ve defined intents so the NLU can classify user inputs.

How to use

Explore threshold types, then set them up.

Explore threshold types

Reconfirmation threshold specifics

The reconfirmation threshold behaves differently depending on whether a confirmation sentence exists in your intent:
  • With a confirmation sentence, the reconfirmation threshold triggers the confirmation sentence.
  • Without a confirmation sentence, the reconfirmation threshold confirms the user input.

Set up thresholds

You can configure thresholds at either the flow or project level. To set them up:
  1. In the navigation menu, go to Design > Flows and open the flow.
  2. Go to the Settings tab, then select Configuration.
  3. Activate Thresholds. As soon as you activate it, this setting overrides the threshold values set up at the project level.
  4. Adjust the handles of the Intent Threshold slider:
    • Reconfirmation Threshold (default: 0.2):
      • Move the handle left to increase reconfirmation prompts.
      • Move the handle right to reduce reconfirmation prompts.
    • Confidence Threshold (default: 0.4):
      • Move the handle left to allow lower-confidence matches.
      • Move the handle right to require higher confidence for matches.
  5. Click Save changes.

Example

Imagine a user asks Can you book a flight? in the chat with the agent. The NLU model assigns a score to the BookFlight intent:

More information

Last modified on November 11, 2026