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

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