Multi-turn Prompting

1. Introduction

Multi-turn prompting is a technique in prompt engineering that involves maintaining context over multiple interactions between the user and the AI model. This allows for more coherent and contextually relevant responses.

2. Key Concepts

2.1 Definitions

Prompting: The act of providing input to an AI model to elicit a desired response.
Multi-turn: Refers to a dialogue that consists of several exchanges between the user and the AI, where previous exchanges influence future responses.
Context: The information that informs the AI's understanding of the conversation, which can include previous messages and user intents.

3. Step-by-Step Process

3.1 Building Multi-turn Prompts

Define the conversation objective.
Collect relevant user inputs and responses.
Maintain state/context of the conversation using data structures (like lists or dictionaries).
Formulate prompts that incorporate the conversation history.
Iterate based on user feedback and adjust prompts accordingly.

3.2 Example Flowchart


graph TD;
    A[Start] --> B{User Input?};
    B -- Yes --> C[Store Input];
    C --> D[Generate Response];
    D --> E{More Questions?};
    E -- Yes --> B;
    E -- No --> F[End];

4. Best Practices

Tip: Always keep track of the conversation context to ensure responses are relevant and coherent.

Use clear and concise language in prompts.
Regularly update context based on user interactions.
Test prompts with different scenarios to enhance robustness.
Encourage user follow-ups to refine the conversation flow.

5. Code Example

Here is a simple Python example of handling multi-turn prompting:


conversation_history = []

def generate_response(user_input):
    # Simulated AI response based on user input
    return f"AI Response to: {user_input}"

while True:
    user_input = input("You: ")
    conversation_history.append(user_input)
    response = generate_response(user_input)
    print(f"AI: {response}")

6. FAQ

What is the main goal of multi-turn prompting?

The main goal is to maintain context and coherence in conversations over several turns, leading to more relevant and useful interactions.

How do I handle context in multi-turn conversations?

You can handle context by storing user inputs and previous responses in a structured format, allowing the AI to reference them when generating new responses.

Can multi-turn prompting be applied to all types of AI models?

While many AI models can benefit from multi-turn prompting, the effectiveness may vary based on the model's architecture and training.