LLM Role-Playing Conversations

14 min readFeb 13, 2025

What if I told you there’s a way to give Google Gemini an “ensemble cast”? What if you could have Gemini play multiple roles, each with its specific area of expertise, and have them discuss your problem, debate different approaches, and refine ideas until they reach a consensus? It’s like assembling your own personal AI dream team, ready to tackle any challenge you throw at them!

In this post, we’ll dive deep into LLM role-playing conversations, showing you how to set up and manage these powerful interactions using Google Gemini and a bit of Node.js magic. We’ll cover the underlying theory (why this works so well), the practical coding steps (don’t worry, it’s not as scary as it sounds!), and many real-world applications to get your creative juices flowing.

Note: This article is more techy than my previous articles about Google Gemini Advanced. However, you don’t need to be a developer to use any of the tools mentioned.

Why Role-Playing Works

So, we’ve established that having Gemini talk to itself is cool, but why does it lead to better results? Let’s ditch the single-question-single-answer approach for a second and think about how we humans solve problems in the real world.

Think about a movie set. You’ve got the screenwriter crafting the story and dialogue. Then you’ve got the director visualizing the scenes, working with the actors, and ensuring everything comes together. They might have disagreements and challenge each other, but ultimately, that collaborative process — that push and pull — leads to a much better film than if either of them worked in isolation.

There are several advantages to this approach:

Specialized Expertise: This is the one-two punch that makes role-playing effective. You’re not just getting a general-purpose AI; you’re creating a specialist and forcing it to stay in its lane. You get focused, relevant, expert-level responses, drastically reducing rambling and generic output. Define exactly who your LLM is (experience, skills, style), and watch the quality soar.
Enhanced Creativity: Think of your best brainstorming sessions with dynamic back-and-forth, where ideas build on each other. That’s what we’re recreating. By mimicking real-world collaborative scenarios (writer/director, coder/tester), you unlock a level of creativity that’s hard to achieve with single-prompt interactions. The “yes, and…” principle in action.
Deeper Exploration: Multiple perspectives force a more thorough examination of the problem. You’re not just accepting the first answer; you’re getting ideas vetted, challenged, and refined through (simulated) debate. This uncovers hidden insights and leads to stronger, more well-thought-out solutions.
Bias Mitigation: We all have blind spots, and AI models can inherit biases from their training data. By intentionally designing roles with differing, even opposing, viewpoints, you actively combat prejudice and ensure a more balanced, ethical, and comprehensive outcome.

Introducing the Power of Roles:

Instead of treating Gemini like a magic 8-ball, we will treat it like a cast of characters. Each Gemini instance we create will perform a specific role, like casting actors in a play. Each actor has a character to portray, with their unique background, motivations, knowledge, and even a specific way of talking.

It is crucial to be hyper-specific with role definitions. Don’t just tell Gemini to “act like a chef.” That’s way too vague. Instead, paint a detailed picture:

“You are a Michelin-starred chef specializing in modern French cuisine. You are known for your innovative use of seasonal ingredients and meticulous attention to detail, bordering on obsession. Your goal is to create an elegant and surprising tasting menu, pushing the boundaries of traditional French cooking while respecting its heritage. You are confident in your abilities but also open to constructive criticism.”

It is not enough to define the role; you should be explicit regarding tone and professionalism.

“Communicate like a friendly mentor offering constructive feedback, always start positive, and be specific with criticism.”

You’re setting the stage for a productive and insightful LLM role-playing conversation by carefully crafting these role definitions. You’re giving Gemini the tools to become those roles and collaborate effectively. In the next section, we’ll get our hands dirty with some code and see how to implement this in Node.js.

Setting Up Your AI Dream Team

Let’s get dirty and build the engine to power our multi-role LLM conversations! We’ll be using Node.js and the Google Gemini API. Don’t worry if you’re not a coding expert; I’ll break down the steps and explain the core concepts. This code is designed to be easy to understand and modify so that you can experiment quickly.

What We’re Building:

We’re creating a ConversationSimulator class. Think of it as a director for our AI actors. It handles these key tasks:

Creating Characters: Setting up each Gemini instance’s role and personality (like casting actors for a play).
Managing the Conversation: Passing messages back and forth between the characters, like a well-orchestrated relay race.
Detecting Consensus: Checking if the characters have reached an agreement and ending the conversation appropriately.
Keeping Track of Turns: Make sure the conversation doesn’t go on forever and provide a summary.

The Code:

const { GoogleGenerativeAI } = require('@google/generative-ai');

// Create a simple class to manage conversations between LLM instances
class ConversationSimulator {
  constructor(apiKey) {
    this.genAI = new GoogleGenerativeAI(apiKey);
    this.model = this.genAI.getGenerativeModel({ model: 'gemini-pro' });
    this.CONSENSUS_MARKER = '[CONSENSUS]';
  }

  // Create a character with a specific role
  async createCharacter(role, personality) {
    const chat = this.model.startChat({
      history: [],
      generationConfig: {
        temperature: 0.9,
        maxOutputTokens: 1000,
      },
    });

    // Initialize the character with their role and personality
    await chat.sendMessage(
      `You are ${role}. ${personality} Please stay in character during our conversation. 
       Respond in a natural conversational way without mentioning that you are an AI.`
    );

    return chat;
  }

  // Check if consensus was reached
  hasReachedConsensus(message) {
    return message.includes(this.CONSENSUS_MARKER);
  }

  // Simulate a conversation between two characters
  async simulateConversation(character1, character2, topic, maxTurns = 5) {
    const conversation = {
      turns: [],
      consensusReached: false,
      finalTurn: 0
    };

    let currentSpeaker = character1;
    let currentListener = character2;
    let currentMessage = `Let's discuss: ${topic}`;

    for (let turn = 0; turn < maxTurns; turn++) {
      console.log(`\nTurn ${turn + 1}:`);
      console.log(`Message: ${currentMessage}\n`);

      // Get response from current speaker
      const response = await currentListener.sendMessage(currentMessage);
      const responseText = response.response.text();

      // Store the conversation turn
      conversation.turns.push({
        speaker: turn % 2 === 0 ? 'Character 1' : 'Character 2',
        message: responseText
      });

      console.log(`Response: ${responseText}\n`);

      // Check for consensus
      if (this.hasReachedConsensus(responseText)) {
        console.log('Consensus reached! Ending conversation.');
        conversation.consensusReached = true;
        conversation.finalTurn = turn + 1;
        break;
      }

      // Switch speakers for next turn
      [currentSpeaker, currentListener] = [currentListener, currentSpeaker];
      currentMessage = responseText;
      if (turn > maxTurns / 2) {
        currentMessage += '\n\n' + `Note: If we reach a consensus, either of us can end our response with "${this.CONSENSUS_MARKER}"`;
      }
    }

    // If we reached max turns without consensus
    if (!conversation.consensusReached) {
      conversation.finalTurn = maxTurns;
    }

    return conversation;
  }
}

// Example usage
async function main() {
  // Replace with your actual API key
  const simulator = new ConversationSimulator('YOUR_API_KEY');

  // Create two characters
  const writer = await simulator.createCharacter(
    'a passionate screenwriter',
    'You are creative, detail-oriented, and care deeply about storytelling. You want to create the best possible story while being open to collaboration.'
  );

  const director = await simulator.createCharacter(
    'an experienced film director',
    'You are visually-minded, practical, and focused on bringing stories to life on screen. You are collaborative and solution-oriented.'
  );

  // Start a conversation between them
  const conversation = await simulator.simulateConversation(
    writer,
    director,
    'Ideas for a chase sene in our upcoming thriller movie.',
    10  // Maximum number of conversation turns
  );

  console.log('\nFull Conversation Summary:');
  conversation.turns.forEach((turn, index) => {
    console.log(`\n${turn.speaker}:`);
    console.log(turn.message);
  });

  console.log('\nConversation Stats:');
  console.log(`Total Turns: ${conversation.finalTurn}`);
  console.log(`Consensus Reached: ${conversation.consensusReached}`);
}

// Run the simulation
main().catch(console.error);

Key Concepts and Explanations:

Setup:

API Key: Get your API key from Google AI Studio and replace ‘YOUR_API_KEY’ in the code.

ConversationSimulator Class: This class is the core of our system.

constructor(apiKey): Sets up the Gemini API connection.
createCharacter(role, personality): Creates a Gemini chat instance and gives it its initial role and personality. This is where you define your “actors.”
hasReachedConsensus(message): Checks if a message contains the [CONSENSUS] marker.
simulateConversation(character1, character2, topic, maxTurns): This is the main function that runs the conversation:
— It sets up two characters, a topic, and a maximum number of turns.
— It loops, letting each character respond to the previous message.
— It checks for consensus after each turn.
— It subtly prompts for consensus after a few turns.
— It keeps track of the conversation history.

main() Function: This is where we put everything into action.

It creates an instance of the ConversationSimulator.
It creates two characters (a screenwriter and a director in this example).
It starts the conversation using simulateConversation.
It prints the results to the console.

Running the Code:

Save: Save the code as a .js file (e.g., gemini_convo.js). The code is also available as a GitHub Gist.
Install: Open your terminal or command prompt and navigate to the directory where you saved the file. Then, run “npm install @google/generative-ai” to install the necessary library.
Run: Execute the code by running “node gemini_convo.js” in your terminal.

This detailed explanation should make the code clear and accessible, even for readers who haven’t seen previous versions. It emphasizes the key concepts, best practices, and the new consensus detection feature. The step-by-step breakdown of the primary function shows how to use the ConversationSimulator class to create and run a multi-role conversation. The instructions on running the code are clear and concise.

Example: Screenwriter + Director

Now for the fun part — let’s see this whole LLM role-playing thing in action! We’ll use the Screenwriter and Director scenario to illustrate setting up roles, managing the conversation, and achieving a concrete goal.

Scenario Setup:

Our objective is simple: Develop a compelling logline for a historical drama about the invention of the printing press. Sounds straightforward. But we want more than just a logline; we want a great logline. And that’s where our AI dream team comes in.

Role Definitions:

Before we unleash our LLMs, we need to define their roles very specifically. Remember, the more detail we provide, the better they’ll perform.

The screenwriter role:

“You are a seasoned screenwriter with 25 years of experience in independent film and historical dramas. You specialize in crafting character-driven stories with complex themes and nuanced dialogue. You are known for your ability to create emotionally resonant narratives that connect with audiences on a deep level. You aim to develop a concise and captivating logline, highlighting the central conflict and the protagonist’s journey. You are confident in your ideas and open to constructive criticism and collaboration. You communicate clearly and professionally.”

The director role:

“You are an award-winning film director with a reputation for visual storytelling and a strong focus on historical accuracy. You have a keen eye for detail and a passion for bringing history to life on screen. You are known for your ability to create immersive and visually stunning cinematic experiences. You aim to ensure the logline translates into a compelling, marketable film concept. You are direct, demanding, and respectful of the screenwriter’s creative vision. You provide specific and actionable feedback.”

Notice how we’ve given each role a distinct personality, expertise, and communication style. This is key to getting them to interact realistically and productively.

Instructions:

Now, provide the instructions for a constructive conversation:

“You are ready to have a constructive conversation. After your discussion, if you reach a consensus, end your response with [CONSENSUS]. Only reach a consensus after hearing from the other party.”

We also need a context for the discussion:

“Let’s brainstorm a logline for a historical drama about the invention of the printing press.”

Embrace the Iterative Process

The power of this technique lies in the process of refinement. Don’t expect the perfect logline on the first try. It’s the back-and-forth, the challenges, and the revisions that lead to the best outcome. Each agent brings a different perspective.

This example demonstrates the core principles of LLM role-playing conversations. By carefully defining roles, managing the flow of information, and encouraging iterative refinement, we can leverage the power of Gemini to achieve results that would be difficult, if not impossible, to accomplish with single-prompt interactions. The roles stay consistent, and they remain focused. The Node.js code is what made this exchange possible.

Emergent Behaviors in Successful LLM Dialogues

We’ve set the stage with well-defined roles and a solid conversational structure. But what happens when we let the LLMs loose? Some pretty remarkable behaviors emerge organically, even without us explicitly programming them. These aren’t just guidelines; they’re what we’ve observed in successful multi-role LLM interactions.

Positive Collaboration:
Interestingly, when roles are clearly defined, and the overall goal is collaborative (without explicitly being told to), the LLMs adopt a positive and encouraging tone. We consistently see phrases like:
— “This is fantastic feedback!”
— “I appreciate you digging into…”
— “You’ve nailed it!”
— “I’m thrilled with how much depth we’ve added.”

This suggests that LLMs, given the proper context, can foster a sense of teamwork without explicit instructions to be “nice.” This emergent positivity is crucial for a productive exchange of ideas.

Think of these as the hallmarks of a productive AI collaboration:

Iterative Refinement:
We didn’t have to tell the LLMs to build upon each other’s ideas. Given a starting point and a clear objective, they naturally engage in iterative refinement. The Screenwriter/Director example perfectly illustrates this: each response builds on the previous one, adding layers of detail and improving the logline step-by-step. This emergent “Yes, and…” behavior demonstrates the LLMs’ ability to participate in complex, multi-stage creative processes.
Structured Discussion:
Even without explicit instructions to break down the discussion, we often see the LLMs (particularly in roles like a “Director” or “Analyst”) imposing structure on the conversation. They might focus on specific aspects of a problem one at a time (e.g., character motivations, plot structure, and visual style). This suggests that LLMs, given a well-defined role focused on analysis or critique, can facilitate organized and methodical brainstorming.
Expert-Level Knowledge:
While the role definitions provide the foundation, the LLMs often go beyond the explicitly stated information. They draw upon their vast training data to demonstrate a strong understanding of relevant concepts, terminology, and real-world examples. The “Director” might reference specific films or cinematic techniques, and the “Screenwriter” might use industry jargon — all without being explicitly told to do so. This emergent expertise makes the LLMs valuable creative partners.
Holistic Thinking:
The best LLM interactions don’t get bogged down in narrow details. They often consider multiple facets of a problem, even those not explicitly mentioned in the initial prompt. In our film example, the LLMs spontaneously discussed plot, character, visual style, sound design, and even the texture of historical props. This emergent holistic thinking demonstrates the potential for LLMs to contribute to a comprehensive vision.
Abstract Reasoning:
LLMs can handle abstract concepts quite effectively. We’ve seen them take ideas like “truth” or “memory” and translate them into concrete plot points, character motivations, and visual representations within the context of the screenplay. This emergent ability to bridge the gap between abstract and concrete is crucial for tackling complex themes.
Consensus Seeking:
Given a collaborative goal, the LLMs naturally work towards a shared understanding and agreement. The “[CONSENSUS]” marker is a helpful tool, but the underlying tendency to seek consensus seems to emerge organically from the interaction, especially when roles are designed to complement each other.
Creative Sparks:
While drawing on existing knowledge, the LLMs can generate original ideas and make unexpected connections. This emergent creativity is the most exciting aspect of multi-role LLM conversations. These tools can be true partners in the creative process, not just sophisticated imitators.

By understanding these emergent behaviors, we can better design our roles, prompts, and conversational frameworks to maximize the potential of multi-role LLM interactions. It’s not just about controlling the conversation; it’s about creating the conditions for these influential, emergent capabilities to flourish. We can then prepare to troubleshoot cases where this does not happen naturally.

Beyond the Silver Screen: Other Applications

We’ve seen the magic of the Screenwriter and Director working together, but the power of two-role LLM conversations extends far beyond filmmaking. Think of it as having a dynamic duo of AI experts ready to tackle various challenges. Here are some compelling applications focusing on powerful two-role pairings:

The Innovator & The Pragmatist:

— Role 1: “You are a visionary innovator, always pushing boundaries and generating wild, unconventional ideas. You’re not afraid to challenge assumptions.”

— Role 2: “You are a pragmatic analyst, focused on feasibility, practicality, and market realities. You provide constructive criticism and identify potential roadblocks.”

— Example: Brainstorming new product features, marketing campaigns, or business models.

The Optimist & The Pessimist:

— Role 1: “You are an optimist who believes in the best possible outcome.”

— Role 2: “You are a pessimist who prepares for the worst possible outcome.”

— Example: Brainstorming to be prepared for every situation.

The Proposer & The Critic:

— Role 1: “You propose a new policy/strategy/idea. You are passionate and articulate in your advocacy.”

— Role 2: “You are a thoughtful critic, examining the proposal for potential flaws, unintended consequences, and alternative perspectives.”

— Example: Debating ethical dilemmas, evaluating policy proposals, analyzing strategic decisions.

These are just a few starting points. The key is to identify two distinct roles that, through their interaction, can achieve a specific goal more effectively than a single LLM could. Get creative with your role definitions, experiment with different pairings, and discover the power of two! Remember always to be very specific when describing the roles and control the flow of the conversation.

Troubleshooting and Common Pitfalls

Okay, we’ve painted a pretty rosy picture of LLM role-playing LLM bliss. But let’s be honest, things don’t always go perfectly smoothly. Sometimes, your AI dream team can turn into disarray. Here are some common pitfalls to watch out for and how to troubleshoot them:

Runaway Conversations:
This is the most common issue. Your LLMs get stuck in a loop, endlessly repeating themselves or going off on bizarre tangents. It’s like watching a conversation between two toddlers who can’t agree on anything. Solutions:
— Maximum Turns: Implement a counter that is checked against a maximum value. Seriously, this is your safety net. Set a reasonable limit on the number of back-and-forth exchanges.
— Strong Role Definitions: Make sure your role definitions are crystal clear and that you’re reinforcing them in every prompt. You don’t have to repeat the entire role description; emphasizing the role’s name is enough. Even if it feels repetitive, it’s crucial for keeping the LLMs on track.
— Clear Prompts: Vague prompts lead to vague responses and can quickly derail the conversation. Be specific about what you want each LLM to do.
— Context Management: Ensure you properly include the relevant conversation history in each prompt. A lost context is a lost LLM.

Vague or Generic Responses:
You’ve set everything up, and the conversation is flowing, but the LLMs are churning out bland, uninspired responses. This often happens when the role definitions or prompts aren’t specific enough. Solutions:
— More Detailed Role Definitions: Add more personality, expertise, and constraints to your role definitions. The more specific you are, the more specific the responses will be.
— More Specific Prompts: Instead of asking, “What do you think?”, ask, “What are the three main weaknesses of this logline, and how would you specifically improve them?”.
— Provide Examples: If you want the LLM to generate something in a particular style, give it examples! This is especially helpful for creative tasks.

Lack of a Clear Objective:
If your agents don’t know what they are working towards, the conversation will meander and not produce an output. Solutions:
— Make sure that the goal is explicit.
— Make sure that the goal is measurable.

These are some of the most common issues you’ll encounter. Don’t be discouraged if things don’t work perfectly right away. Experimentation, troubleshooting, and iteration are all part of the process. The key is to be patient and persistent and learn from mistakes.

Conclusion

You’ve now got the knowledge (and hopefully the inspiration) to go beyond basic prompting and unlock the true potential of Google Gemini with LLM role-playing conversations. We’ve covered a lot of ground, from the underlying theory of role-playing to the practical steps of setting up a Node.js environment and even some common troubleshooting tips.

The key takeaway is that you’re no longer limited to interacting with Gemini as a single, all-purpose entity. You can now create a team of specialized AI agents with unique perspectives, expertise, and communication styles.

The power of this technique lies in the interaction between the roles. The back-and-forth, the challenges, the refinements, and the eventual consensus (or informed decision) lead to truly remarkable results.

Check out my reading list of other Google Gemini articles.

This post was created with the help of AI writing tools, carefully reviewed, and polished by the human author.