Multi-Turn Conversation Design: Keeping Long Exchanges On Track
Multi-turn conversations drift in ways one-shot prompts do not. Here is how I design prompts that survive long exchanges. Multi-turn prompting is designing for a conversation rather than a single request. Most prompting advice focuses on one-shot prompts, but real applications are conversations: support chats, research assistants, and agents that gather information across several exchanges. Multi-turn prompts fail in ways one-shot prompts do not, because the model accumulates context that drifts, forgets earlier constraints, and gets confused by contradictory messages. After building several conversational LLM features, I have a set of design patterns that keep multi-turn conversations on track. This guide covers them. The Context Drift Problem In a long conversation, the model weights recent messages more heavily than early ones. A constraint set in the first message fades as the conversation grows, and by turn ten the model may have stopped following it entirely. This is not a memory bug. It is how attention works over long contexts. The fix is not to expect the model to remember, but to reinforce critical constraints in the system prompt so they appear in every turn, not just the first. System prompt carries permanent constraints that apply to every turn. Per-turn instructions carry what is specific to the current exchange. Conversation history carries context the model should reference.