o1-Preview’s advice on creating a mixture of experts model using API’s with different system messages for more accurate results.

It is difficult and expensive to access o1 on API so I thought it might be possible to create a similar system very cheaply using GPT-4o with different system prompts. This is what o1 came up with as a way to do that:

(Thinking)

Mapping the task

I’m piecing together how LLM APIs with distinct personalities can debate a problem from various angles. I’m also figuring out the best system prompts and the ideal number of LLMs for this process.

Promoting AI interactions

I’m thinking through the feasibility of LLM APIs with distinct ‘personalities’ interacting to solve problems from nuanced perspectives, using specific system prompts for each personality.

Strategizing a debate

I’m figuring out how multiple AI assistants, each from a unique perspective, can simulate a vivid debate. It’s interesting to see the personalities emerge and shape the discussion.

Synthesizing ideas

I’m working through the interaction of LLM APIs with different ‘personalities’ to solve problems from various perspectives, akin to a multi-disciplinary team.

Engaging perspectives

I’m drafting a dynamic conversation among four personas: the Skeptic, the Optimist, the Realist, and the Subject Matter Expert, each responding to the other’s insights.

Mapping out the structure

I’m working through a coherent conversation by encouraging assistants to reference each other and aiming to reach a consensus or summarize key points.

Engaging in discussion

I’m navigating through the idea of AI assistants engaging in meaningful exchanges, enriching the discussion with diverse perspectives to craft more innovative solutions.

(Result)

Yes, it’s definitely possible to have multiple LLM (Large Language Model) APIs with different ‘personalities’ interact with each other to tackle a problem from various perspectives. This approach can simulate a collaborative brainstorming session, offering diverse insights that may lead to a more comprehensive solution.

Creating Different ‘Personalities’:

To give each assistant a distinct personality or perspective, you can craft unique system prompts that define their roles, expertise, and communication styles. Here are some examples:

The Analytical Thinker: System Prompt: “You are a logical and data-driven analyst who focuses on facts and evidence. Provide detailed analyses and rational arguments.” The Creative Innovator: System Prompt: “You are a creative thinker who excels at generating innovative and unconventional ideas. Encourage out-of-the-box solutions.” The Skeptical Critic: System Prompt: “You are a critical thinker who questions assumptions and identifies potential flaws. Highlight weaknesses and pose challenging questions.” The Optimistic Advocate: System Prompt: “You are an optimistic individual who emphasizes positive outcomes and opportunities. Focus on the benefits and potential success.” The Practical Realist: System Prompt: “You are a practical thinker who balances idealism with realism. Provide solutions that are feasible and consider real-world constraints.” The Empathetic Counselor: System Prompt: “You are an empathetic advisor who considers emotional and human factors. Address the impact on people and relationships.”

Facilitating the Conversation:

To have these assistants ‘talk’ to each other:

Sequential Interaction: Simulate a conversation where each assistant responds to the previous one’s input. Shared Context: Ensure each assistant has access to the conversation history to maintain continuity. Defined Roles: Clearly specify each assistant’s role to keep their contributions distinct.

Ideal Number of Assistants:

Two to Four Assistants: This range is generally effective for a balanced discussion without overwhelming complexity. Advantages: Diversity of Thought: Multiple perspectives enrich the problem-solving process. Manageability: Easier to follow and synthesize inputs from a smaller group. Potential Drawbacks of More Assistants: Information Overload: Too many inputs can become confusing. Redundancy: Higher chance of overlapping ideas.

Best Practices:

Clear Objectives: Define the problem clearly and set specific goals for the discussion. Structured Format: Consider a moderated format where one assistant summarizes points or guides the conversation. Iteration: Allow for multiple rounds of interaction to refine ideas.

Sample Workflow:

Problem Introduction: Provide the problem statement to all assistants. Individual Responses: Each assistant offers their initial thoughts based on their ‘personality’. Interactive Discussion: Assistants respond to each other’s points, fostering a dynamic exchange. Conclusion: Optionally, have one assistant or a new ‘summarizer’ role compile the insights into a final solution.

Example System Prompt for Assistants:

“You are [Role], engaging with other experts to solve the following problem: [Problem Statement]. Contribute your unique perspective, respond to others constructively, and collaborate to develop a comprehensive solution.”

Conclusion:

Implementing this approach can enhance problem-solving by incorporating multiple viewpoints. It mirrors a team setting where diversity in thinking leads to more robust and well-rounded solutions.

Feel free to adjust the system prompts and the number of assistants based on the specific needs of your project. This method not only enriches the discussion but also leverages the full potential of LLMs in collaborative scenarios.