Fine-Tuning Simulation Prompts for Different AI Platforms: A Deep Dive into Platform-Specific Optimization
Introduction
As the creator of Prompt Quest, an AI prompt base simulation platform for learning and training, I’ve discovered something fascinating that every AI enthusiast should know: while a general “one size fits all” prompt might work reasonably well across different AI platforms, there’s tremendous value in fine-tuning your simulation prompts for each specific AI chat system.
Through extensive testing and optimization, I’ve found that ChatGPT, Claude, Gemini, Grok, and Microsoft Copilot each have unique characteristics that, when properly leveraged, can dramatically improve the quality and effectiveness of your simulation experiences. This isn’t just about minor tweaks – it’s about understanding the DNA of each AI platform and speaking their language fluently.
What Are AI Simulation Prompts?
Before diving into the platform-specific optimizations, let’s establish what we’re working with. Simulation prompts are long, detailed instructional prompts designed to create immersive, educational, game-like interactions with AI systems. Think of them as comprehensive rulebooks that transform your AI chat into a dynamic learning environment.
These simulations can cover everything from business strategy and financial planning to historical scenarios and scientific experiments. The key is creating a structured, realistic environment where users can make decisions, face consequences, and learn from their experiences – all while being guided by an AI that understands the rules and objectives of the simulation.
The Platform-Specific Optimization Discovery
My journey into platform-specific optimization began when I noticed subtle but significant differences in how various AI systems responded to identical prompts. While the core simulation would run on all platforms, the quality of interaction, the depth of analysis, and the overall user experience varied considerably.
This led me to systematically test and refine my approach for each platform, discovering that what makes Claude excel might actually hinder ChatGPT’s performance, and vice versa. The results were eye-opening and have since become a cornerstone of my prompt engineering methodology.
ChatGPT: The Conversational Optimizer
Testing conducted with GPT-4.1 (flagship model for complex tasks, released April 2025) and o3 (powerful reasoning model, full release April 2025)
Key Optimization Strategies
Conversational Flow Over Rigid Structure ChatGPT thrives on natural language flow, even within complex structured prompts. Instead of using heavily hierarchical headers, I’ve found success with more conversational transitions. For example, rather than using technical section dividers, ChatGPT responds well to headers like “Game Mechanics & Realism” that feel more natural while maintaining clarity.
Consolidation and Streamlining ChatGPT can handle complexity, but it performs better when related concepts are grouped together. I typically consolidate “Simulation Scope,” “My Role,” and “My Objective” into a flowing narrative rather than treating them as completely separate entities. This reduces cognitive load while maintaining all necessary information.
Emphasis on Adherence ChatGPT benefits from having key constraints reiterated within descriptive text rather than relying solely on separate rules sections. For instance, when defining the AI’s role, I explicitly embed phrases like “adhering strictly to real-world logic, psychology, risk, and consequences” directly into the role description.
Actionable Closing Questions Instead of generic “ask me any questions you need,” ChatGPT responds better to specific, actionable prompts like “Are there any specific financial starting conditions you’d like to begin with? This helps me customize the scenario.” This direct approach leads to more focused and useful initial interactions.
Why These Changes Work
ChatGPT’s architecture and training make it particularly responsive to natural language patterns. It excels at “thinking step-by-step” when properly guided and can handle very long, complex prompts with excellent context retention. The key is maintaining logical flow while reducing unnecessary hierarchical complexity.
Claude: The Structured Perfectionist
Testing conducted with Claude 4 Opus and Claude 4 Sonnet (both released May 22, 2025)
Key Optimization Strategies
XML Tags Are Your Best Friend This is the most significant change for Claude optimization. Every major section should be enclosed in XML-like tags: <simulation_scope>, <my_role_and_objective>, <game_rules>, <your_role_as_game_master_and_mentor>. Claude is specifically fine-tuned to pay attention to these tags, making it easier to parse complex instructions.
Polite but Firm Guidance Claude’s Constitutional AI design means it responds better to instructions that are firm but respectful. Instead of “call them out,” I use phrases like “politely but firmly reject” or “gently but clearly point this out and explain the real-world implications.” This aligns with Claude’s helpful and ethical nature while maintaining the necessary strictness.
Explicit Reasoning Requirements Claude excels at providing explanations and reasoning. I explicitly ask for “why” in rejection scenarios: “Explain why a request is unrealistic based on real-world principles.” This leverages Claude’s strength in logical explanation while ensuring the learning objective is met.
Refined Role Terminology Instead of generic terms like “The Game,” I use more descriptive and purposeful terms like “Game Master and Mentor.” This resonates with Claude’s instructional nature and clearly defines its educational responsibilities.
Why These Changes Work
Claude’s strengths lie in complex instruction following, nuanced reasoning, and maintaining ethical guidelines. The XML tags provide clear structure, while the emphasis on explanation and reasoning leverages its natural tendencies toward thorough, helpful responses.
Gemini: The Logical Processor
Testing conducted with Gemini 2.5 Pro (released March 25, 2025, generally available as of June 17, 2025)
Key Optimization Strategies
Concise and Direct Communication Gemini performs best with clear, direct instructions that minimize redundancy. I consolidate related concepts and use strong, actionable verbs throughout the prompt. The opening immediately establishes roles and objectives without unnecessary preamble.
Strict Realism Emphasis Gemini benefits from reinforced constraints. I use phrases like “All outcomes must be strictly realistic” and bold key terms to ensure adherence to simulation boundaries. This helps prevent creative deviations that might compromise the learning experience.
Bulleted Structure for Rules Converting prose rules into clear bullet points makes instructions more scannable and actionable for Gemini. Each bullet becomes a discrete instruction that Gemini can process and remember independently.
Thematic Grouping Instead of separate sections for closely related concepts, I group them thematically. “Simulation Scope & Realism” combines two related ideas, reducing cognitive load while maintaining logical flow.
Why These Changes Work
Gemini’s strength lies in logical reasoning and following precise instructions. The emphasis on structure, clarity, and explicit constraints aligns with its processing style, while the consolidated approach reduces potential confusion or ambiguity.
Grok: The Unfiltered Challenger
Testing conducted with Grok 4 (unveiled July 9, 2025), available in both “Standard” and “Heavy” tiers
Key Optimization Strategies
Leverage Natural Edge Grok is designed with a “witty and rebellious streak,” so I don’t need to explicitly instruct it to be edgy. Instead, I use phrases like “unflinchingly realistic” and “don’t hold back” to tap into its inherent directness without overemphasizing it.
Assertive Command Structure Grok responds well to clear, direct commands using imperative language: “You are,” “You must,” “Your responses must be.” This aligns with its straightforward, no-nonsense approach.
Challenge-Based Engagement I often end Grok prompts with subtle challenges like “Prove you can run this” to trigger its competitive nature and encourage more engaged, rigorous responses.
Consolidated Directives Rather than separating different aspects of its role, I combine them under unified headings like “Your Core Directives” to emphasize their integrated nature.
Why These Changes Work
Grok’s design philosophy centers on being direct, unfiltered, and challenging. These optimizations work with its natural personality rather than against it, resulting in more authentic and effective interactions.
Microsoft Copilot: The Productivity Assistant
Testing conducted with Microsoft Copilot, which integrates various OpenAI models including the GPT-4 series and GPT-4o
Key Optimization Strategies
Task-Oriented Framing Copilot excels when interactions are framed as assistance requests. I begin with “User Intent: I need your assistance to…” which immediately aligns with its assistive function.
Clear Role Definitions Copilot performs best with unambiguous role definitions. I use terms like “Simulation Engine & Strategic Advisor” and provide explicit “Key Responsibilities & Directives” in bulleted format.
Actionable Output Emphasis Instead of generic outcomes, I request “concise and actionable updates” and “constructive feedback” that directly feeds into user productivity and learning.
Structured Information Flow Using clear headings and bullet points makes the prompt easy for Copilot to parse and act upon efficiently, similar to how it processes instructions in productivity applications.
Why These Changes Work
Copilot’s design as a productivity enhancer means it responds best to clear, task-oriented instructions that result in actionable outputs. The emphasis on assistance and constructive feedback aligns with its core function.
Implementation Best Practices
Start with Your Base Prompt
Begin with a solid, comprehensive simulation prompt that works reasonably well across platforms. This serves as your foundation for platform-specific optimizations.
Test Incrementally
Don’t try to implement all optimizations at once. Make changes gradually and test the impact of each modification on the quality of your simulation experience.
Monitor Response Quality
Pay attention to how each platform responds to your optimizations. Look for improvements in:
- Adherence to simulation rules
- Quality of feedback and analysis
- Engagement level and personality
- Learning value of interactions
Iterate and Refine
Platform optimization is an ongoing process. As AI systems evolve, so too should your optimization strategies. Regular testing and refinement ensure continued effectiveness.
Conclusion
The art of AI simulation prompt optimization lies in understanding that each platform has its own personality, strengths, and optimal communication style. By recognizing these differences and tailoring your approach accordingly, you can dramatically improve the quality and effectiveness of your simulation experiences.
Whether you’re using ChatGPT’s conversational flow, Claude’s structured reasoning, Gemini’s logical processing, Grok’s unfiltered directness, or Copilot’s task-oriented assistance, the key is speaking each platform’s native language while maintaining the core educational value of your simulation.
The investment in platform-specific optimization pays dividends in the form of more engaging, educational, and realistic simulation experiences. As AI technology continues to evolve, this nuanced approach to prompt engineering will become increasingly important for creating truly effective educational simulations.
Remember: while a one-size-fits-all approach might work, a tailored approach will always work better. The future of AI-powered education lies in understanding and leveraging these platform-specific strengths to create optimal learning experiences for every user.
Ron Stark is the creator of Prompt Quest, an AI prompt base simulation platform dedicated to advancing AI-powered learning and training experiences. Through extensive research and testing, he continues to push the boundaries of what’s possible in AI-driven educational simulations.
