## Reject Intent

The _Reject Intent_ feature allows you to block the NLU model from recognizing user inputs that aren’t relevant to your AI Agent. This approach helps AI Agents focus on in-context questions, ignoring irrelevant ones.

## Key Benefits

- **Enhanced focus on core capabilities**. By filtering out irrelevant inputs, your AI Agent can prioritize the tasks it’s designed to handle.
- **Improved user experience**. By avoiding responses to out-of-scope queries, you can prevent confusion and keep conversations on track.

## Restrictions

- This feature may struggle with complex or varied off-topic inputs unless paired with [standard Intents](https://docs.cognigy.com/ai/platform-features/nlu/intents/overview) or [Intent rules](https://docs.cognigy.com/ai/platform-features/nlu/intents/rules).

## Limitations

- You can create only one reject Intent per Flow.

## How to Use

To use this feature, create a reject Intent and add several example sentences.  
**Create a Reject Intent**  
- GUI  
- API

To create a reject Intent, go to **NLU > Intents** in your Flow.  
Under the **Create Intent** button, click  and select **Create ‘Reject Intent’**.

You can create a reject Intent using the [Cognigy.AI API /v2.0/flows//intents](https://api-trial.cognigy.ai/openapi#post-/v2.0/flows/-flowId-/intents) request and specifying the `"isRejectIntent": true` parameter.

### Add Example Sentences
On the **Reject Intent** page, add a list of example sentences representing the user inputs you want the NLU model to ignore. Save changes and click **Build Model** to apply them. You can also generate these sentences [using Generative AI](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/generative-ai). Similar to standard Intents, you can also add [annotations](https://docs.cognigy.com/ai/platform-features/nlu/intents/annotations) to each example sentence.

## How to Test

Test your feature in the Interaction Panel:  
1. Open the Interaction Panel and activate **Debug Mode**.  
2. Enter examples matching your reject Intent sentences to verify if they’re ignored by the NLU model. If the NLU model catches them, the Input object will have the following format:
   
   ```
   "nlu": {
       "intentMapperResults": {
         ...
         "intentPath": [
           "Reject Intent"
         ],
         "scores": [
           {
             "id": "f0db738b-e1c3-4841-a6d9-490e082b8ec5",
             "name": "Reject Intent",
             "score": 1,
            ...
           }
         ]
       },
   ```
## Example

Imagine your AI Agent is designed to assist drivers with parking-related queries.  
A user asks `Where can I find a park nearby?` intending a green space rather than a parking lot.  
By including similar reject Intent sentences, you can train the AI Agent to filter out these requests, ensuring it stays focused on parking assistance.

### More Reject Example Sentences

| **Reject Intent Name** | **Example Sentences** |
| --- | --- |
| `OutOfScope_Parks` | - Where can I find a park nearby?<br>- Are there any green spaces around here? <br>- Is there a park I can visit close by? <br>- Where’s the nearest nature spot? <br>- Can you point me to a local park? <br>- Are there any nice parks in this area? <br>- Where can I take a walk in a park nearby? |

## More Information

- [Intents](https://docs.cognigy.com/ai/platform-features/nlu/intents/overview)  
- [Intent Rules](https://docs.cognigy.com/ai/platform-features/nlu/intents/rules)

Last modified on April 21, 2026