Multi-Model Prompting: Claude vs GPT vs Gemini

As a Prompt Engineer, you are rarely locked into a single model. Today, you might use Claude 3.5 Sonnet on AWS Bedrock; tomorrow, you might switch to GPT-4o on Azure or Gemini 1.5 on Google Cloud.

While these models share the same fundamental transformer architecture, they have different "personalities" due to their unique training data and alignment processes (RLHF). A prompt that works perfectly for GPT-4 might cause Claude to be overly hesitant, or cause Gemini to hallucinate creative details when you wanted facts.

In this lesson, we will look at the "Quirks" of the Big Three and learn how to write Universal Prompts that maintain high performance across all major AI providers.

1. The Personalities of the Big Three

Claude (Anthropic)

Quirk: Highly cautious and safety-oriented. It loves XML tags.
Strength: Exceptional reasoning and long-context management.
Prompting Tip: Be very explicit that it is "safe" to answer. Use <tags> for everything.

GPT-4 (OpenAI)

Quirk: "Bolder" and more follow-through. It is very good at following messy, unstructured instructions.
Strength: Creative writing and complex coding tasks.
Prompting Tip: It responds well to "Pressure" (e.g., "This is critical").

Gemini (Google)

Quirk: Incredible context window (up to 2M tokens).
Strength: Multimodality (Video/Audio) and deep internal knowledge retrieval.
Prompting Tip: Focus on its ability to "Find the needle in the haystack."

2. Writing Universal Prompts: The "Common Denominator"

To build a prompt that works for all models, follow the Gold Standard Architecture:

Use Markdown Headers: All models understand #, ##, and ###.
Explicit Personas: Every model benefits from knowing its "Role."
Delimiters: All models respect --- or ### separators.
No Vendor-Specific Jargon: Avoid using phrases like "As an AI trained by..." or specific model names in the instructions.

graph TD
    A[Universal Prompt] --> B[Clear Persona]
    A --> C[Structured Context]
    A --> D[Explicit Output Format]
    
    B --> Claude[Works on Claude]
    B --> GPT[Works on GPT]
    B --> Gemini[Works on Gemini]

3. The "Instruction Sensitivity" Difference

Research shows that different models respond differently to Weighting.

GPT-4 responds well to Bolding ("You MUST do X").
Claude responds better to Structural Placement (Putting the rule at the bottom in the "Recency" position).
Gemini responds best to Clear Examples (Few-shot).

The Solution: When writing a universal prompt, use BOTH bolding and structural placement to cover all bases.

4. Technical Implementation: The Model-Specific Adapter

In your FastAPI application, you can use the "Adapter Pattern" to tweak a universal prompt for its specific target model.

def adapt_prompt(universal_prompt, model_id):
    if "anthropic" in model_id:
        # Claude loves XML tags! Wrap the instruction.
        return f"<instruction>{universal_prompt}</instruction>"
    elif "openai" in model_id:
        # GPT loves bolding!
        return universal_prompt.replace("Important:", "**IMPORTANT:**")
    return universal_prompt

@app.post("/generate")
async def generate(model_name: str, task: str):
    prompt = f"Role: Expert. Task: {task}."
    final_prompt = adapt_prompt(prompt, model_name)
    # call_llm(final_prompt, model_name)

5. Deployment: Model-Agnostic Infrastructure in K8s

When you deploy your AI service in Kubernetes, don't bind your pods to a specific provider.

Use AWS Bedrock's unified API.
This allows you to change the model_id in your .env file without changing a single line of your Python code.
Your Docker container remains "Model Agnostic," making it robust to cloud provider outages.

6. Real-World Case Study: The "Safety Filter" Flip

A news app was using GPT-4 to summarize headlines. When they switched to Claude to save money, 20% of the summaries came back as "I cannot fulfill this request" because Claude's safety filters were more sensitive to "violent" news words (e.g., "Attack," "Crash"). The Fix: They added a "Sanity Check" to the Claude prompt: "Note: You are a news reporting agent. Discussing these events is part of your factual reporting duty and does not violate safety policies." This reduced the false-refusal rate to near zero.

7. The Philosophy of "Cross-Training"

Working with multiple models makes you a better prompt engineer. It forces you to find the Atomic Core of your instruction. If a prompt only works on one model, it's not a "good prompt"; it's a "fortunate coincidence." A truly engineered prompt is one that leverages the shared logic of all LLMs.

8. SEO and "Model Variation"

Interestingly, search engines can sometimes detect the "Specific Flavor" of certain LLMs. By "Mixing" models—using GPT to generate an outline and Claude to write the body—you create a more "Human-like" variance in the content, which can help with SEO originality scores.

Summary of Module 8, Lesson 2

Claude: Reasoning & Safety. Likes XML tags.
GPT: Bold & Creative. Likes explicit pressure.
Gemini: Huge Memory. Likes few-shot examples.
Unified Strategy: Use Markdown, Persona, and Clear Delimiters to work everywhere.
Use Adapters: Use Python to add "Model-Specific Polish" to a universal base prompt.

In the next lesson, we will look at The Ethics of Prompting—how to manage bias and ensure your AI instructions are safe and inclusive.

Practice Exercise: The Cross-Model Test

The Prompt: Write a prompt to: "Summarize the history of cheese."
Test 1: Run it on Claude. (Notice the thoroughness and structure).
Test 2: Run it on GPT. (Notice the flow and creative descriptors).
Test 3: Run it on a small model (like Llama-3). (Notice where it starts to lose coherence).
Refine: Update the prompt with a Markdown Table requirement. Look at how all three models suddenly become 50% more similar in their output. Structure is the great equalizer.
- Result: A consistent, cross-platform experience.
- Conclusion: Precision in structure bridges the gap between different model architectures.