# Intent Matcher

The Intent Matcher node uses LLM capabilities to understand the meaning behind user inputs and route conversations accordingly. When a user message arrives, this node analyzes it against defined intents and directs the flow based on the best match.

#### Setting up intent matching:

1. Select the LLM model to use for intent analysis
2. Define intent name (e.g., "contact-team", "request-pricing")
3. Describe trigger situations that should activate this intent
4. Create different paths for each intent
5. Set up a default path for unmatched intents

Example: A virtual being needs to distinguish between product inquiries and support requests:

<figure><img src="/files/NFP0rq0d0d0OwKtAHJdC" alt=""><figcaption><p>Example of use of  Intent Matcher node</p></figcaption></figure>

```
Intent: product-inquiry
Trigger: When user asks about products, features, or pricing

Intent: technical-support
Trigger: When user mentions problems, errors, or needs help

Default: General conversation handling
```

#### Common use cases:

* Route to specific knowledge base sections
* Direct to appropriate support flows
* Identify user goals and intentions
* Categorize queries by type
* Handle multiple related intents

{% hint style="info" %}
**Important considerations:**

* Keep trigger descriptions clear and specific
* Test with various phrasings of similar intentions
* Consider common user language and expressions
* Balance between too broad and too specific triggers
* Monitor intent matching accuracy
* Maintain a sensible default path for unmatched intents
  {% endhint %}


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