Intent Matcher

Leverages LLM understanding to create natural, context-aware routing without needing exact keyword matches, making conversations more flexible and human-like.

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:

Example of use of Intent Matcher node

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

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