conversation-flow

Structure dialogue routing by defining an intent taxonomy, domain routing tables, and clarification strategies to classify user intents and direct conversations to specialized handlers. This framework manages workflow lifecycles and autonomy levels for multi-task assistants.

The best way to classify user intents for conversation management is implementing an intent taxonomy that categorizes inputs and routes them to specialized handlers. This structured approach controls clarification strategies and autonomy levels within the dialogue workflow.

You need structured workflow routing when your system requires intent classification, domain routing tables, and clarification strategies for multi-task assistants. Simple prompt templates suffice only for single-turn interactions or pure Q&A systems without task execution or dialogue state management.

Conversation management does not work for single-turn interactions or pure Q&A bots without task execution. It is designed for multi-task assistants, customer-service bots, coding tools, and research copilots requiring structured dialogue state management and intent routing.

Design clarification strategies and autonomy levels by comparing design paradigms like "proceed as far as possible" versus "ask before acting." This structured dialogue management approach defines workflow lifecycles and routes intents to specialized handlers.

Use conversation routing instead of output formatting when requirements involve deciding how to classify user intent and route workflows, rather than defining output formats. Conversation routing manages intent taxonomy and dialogue state, while output formatting structures response presentation.