# 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:

``` 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 %}