Multi-turn conversation is a dialogue that extends across multiple exchanges between user and AI system, requiring the system to maintain context and coherence throughout the interaction.

Single-turn interactions (question in, answer out) are easy. Multi-turn conversations are hard. The customer says "I want to return my order." The AI asks which order. The customer says "the one from last week." The AI needs to track that they're discussing a return, identify the correct order, and continue the conversation with that context intact.

Multi-turn capabilities require: context management (tracking what's been said), reference resolution (understanding "it," "that one," "the second thing"), state tracking (where are we in the conversation flow), and coherent continuation (responses that acknowledge conversation history). Quality drops as conversations lengthen—context gets lost, references become ambiguous, the AI repeats itself or contradicts earlier statements.

For support operations, multi-turn performance determines whether AI handles real customer conversations or just simple queries. Most real support interactions span multiple exchanges. Test your AI with realistic multi-turn scenarios: topic switches, clarifications, corrections, compound requests. If it falls apart after three turns, it's not production-ready for complex interactions.

Related terms: Context window, AI agent memory, Conversational AI