Clarification Pattern

You resolve low classification confidence by generating targeted, language-specific clarification questions. The pattern uses a prompt builder, localization, and caching to ask users directly and improve routing accuracy without breaking the conversation flow.

The best way to handle ambiguous intents in multi-agent routing is to generate targeted clarification questions when classification confidence falls below your threshold. This pattern uses fallback mechanisms and localized prompts to disambiguate intents reliably.

Disambiguation in multilingual conversational systems works by applying a flexible prompt builder with localization to generate language-appropriate questions. It caches these prompts to deliver quick, reusable clarifications for the same ambiguous context.

Yes, you can implement fallback strategies for clarification prompts when the LLM is unavailable. The pattern includes specific fallback mechanisms to ensure timely, user-friendly clarifications are still delivered even if question generation fails.

To start generating clarification questions for routing, you need to provide the current classification result and the user language. The pattern uses these inputs to build a concise, context-aware prompt that resolves the detected ambiguity.