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Multi-turn/Conversational Prompts

Multi-turn or conversational prompting involves maintaining context and coherence across multiple exchanges, enabling the model to participate in ongoing dialogues or conversations. This technique is essential for building chatbots, virtual assistants, and any application requiring sustained interaction.

Key Concepts​

  • Context Retention: The model remembers and uses information from previous turns.
  • Dialogue Coherence: Responses are logically connected and relevant to the conversation history.
  • State Management: The prompt includes or references prior exchanges to maintain continuity.
  • Personalization: The model adapts to user preferences and prior statements.

Best Practices​

  1. Include Conversation History

    • Provide relevant previous turns in the prompt.
    • Summarize or condense history if the conversation is long.

  2. Clarify Speaker Roles

    • Use clear labels (e.g., User, Assistant) for each turn.
    • Distinguish between different participants in multi-user scenarios.

  3. Update Context Dynamically

    • Add new information as the conversation progresses.
    • Remove outdated or irrelevant details.

  4. Maintain Consistency

    • Ensure the model’s responses align with previous statements.
    • Correct or clarify misunderstandings as needed.

Examples​

Basic Multi-turn Prompt​

User: "What's the weather like in Paris?"
Assistant: "It's currently sunny and 22°C in Paris."

User: "Will it rain tomorrow?"
Assistant:

Personalization Example​

Earlier in the conversation:
User: "I'm allergic to peanuts."
Assistant: "Thanks for letting me know. I'll avoid suggesting recipes with peanuts."

Current prompt:
User: "Can you recommend a dessert?"
Assistant:

Task Continuity Example​

User: "Help me plan a three-day trip to Rome."
Assistant: "Sure! What are your interests?"

User: "I love history and Italian food."
Assistant:

Common Pitfalls​

  1. Loss of Context

    • Not including enough history for the model to understand the conversation.
    • Forgetting important user preferences or facts.

  2. Context Overload

    • Including too much irrelevant or redundant information.
    • Exceeding the model’s input length limit.

  3. Speaker Confusion

    • Mixing up user and assistant roles.
    • Ambiguous or missing speaker labels.

  4. Inconsistency

    • Contradicting previous responses.
    • Failing to acknowledge or correct errors.

Use Cases​

  1. Chatbots and Virtual Assistants

    • Customer support
    • Personal assistants
    • FAQ bots

  2. Interactive Applications

    • Language learning partners
    • Interview simulations
    • Role-play scenarios

  3. Task Management

    • Step-by-step guidance
    • Workflow automation
    • Multi-step problem solving

  4. Personalization

    • Remembering user preferences
    • Adaptive recommendations
    • Ongoing user engagement

When to Use Multi-turn Prompts​

Multi-turn prompting is ideal when:

  • The application requires sustained, coherent dialogue.
  • User preferences or prior statements must be remembered.
  • Tasks span multiple steps or exchanges.
  • Personalization and continuity are important.

When to Consider Alternatives​

Consider other techniques when:

  • The task is single-turn or does not require context retention.
  • The conversation history is too long for the model’s input window.
  • Simpler, standalone prompts are sufficient.

Tips for Optimization​

  1. History Pruning

    • Remove irrelevant or outdated turns to save space.
    • Summarize long conversations when possible.

  2. Structured Formatting

    • Use clear speaker labels and consistent formatting.
    • Separate each turn for readability.

  3. Test for Coherence

    • Check that the model maintains logical flow and consistency.
    • Revise prompts if the model loses track of context.

  4. Monitor Input Length

    • Be mindful of the model’s maximum input size.
    • Prioritize the most relevant parts of the conversation.