How to Build an Intent Classification Hierarchy

A conversational AI agent can improve customer experience by routing users to the correct intent, but building one requires careful consideration of natural language understanding (NLU) best practices, intent overlap and ambiguity, training set consistency, and hierarchical classification. Implementing a hierarchical structure with two layers can enhance precision and reduce misclassification. Grouping intents by nouns or verbs can improve performance, but handling outliers and broad queries is crucial. AI Studio's subflow feature can help manage agent organization and prevent technical debt when designing hierarchical classification. By following these steps and considering design considerations, developers can create better conversational AI agents that provide a more effective user experience.

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