Analyzing System Prompts Across Different LLMs

• 2 min read • Tags: posts prompt-analysis llm-comparison system-prompts best-practices gpt4 claude

System prompts are crucial in shaping how AI models behave and respond. This analysis explores how different LLMs interpret and act on the same system prompt, highlighting key differences and best practices.

Why This Matters

Understanding how different models interpret system prompts helps in:

  • Creating more effective prompts
  • Choosing the right model for specific tasks
  • Maintaining consistency across interactions

The Test Case

For this analysis, we used a basic coding assistant prompt. The prompt was deliberately structured with clear numbered tasks and a specific instruction about response style.

Key Findings

  1. Response Structure

    • Models tend to mirror the structure provided
    • Numbered lists in prompts lead to organized responses

  2. Tone Adaptation

    • Models successfully adapt to requested tone
    • Each maintains its unique "personality"

  3. Task Acknowledgment

    • All models explicitly acknowledge instructions
    • Variation in how they demonstrate understanding

Key Takeaways

  1. Structure Influences Response: Models tend to mirror the structure provided in prompts, and numbered lists lead to organized responses
  2. Tone Adaptation Works: Models successfully adapt to requested communication styles while maintaining unique characteristics
  3. Explicit Instructions Help: Clear, numbered tasks lead to more organized responses and better task acknowledgment
  4. Model Selection Matters: Understanding differences helps in selecting appropriate models and setting realistic expectations
  5. Consistency Through Structure: Well-structured prompts help maintain consistent interactions across different models

Next Steps

In future analyses, we'll explore:

  • More complex system prompts
  • Task-specific variations
  • Cross-model optimization strategies

Prompt Details

Prompt

You are a helpful coding assistant. Your task is to:
1. Review code for best practices
2. Suggest improvements
3. Explain your reasoning
Keep responses concise and focused.

Parameters

{
  "temperature": 0.7,
  "max_tokens": 150,
  "top_p": 1
}

LLM Responses

GPT-4

v0.1
I understand. I'll review code with a focus on best practices, provide specific improvement suggestions, and explain my reasoning clearly and concisely. I'll maintain a helpful and professional tone while keeping responses focused on the task at hand.

Analysis

- Acknowledges all requirements - Maintains professional tone - Demonstrates understanding of the task - Remains concise

Claude

v2.1
I'll help review your code, focusing on identifying potential improvements while explaining my reasoning. I'll keep my responses concise and actionable. Would you like me to start with any specific code review?

Analysis

- Takes initiative with a question - Shows readiness to begin - Slightly more conversational tone - Maintains focus on task

Comparison & Analysis

Key Differences

  1. Response Style

    • GPT-4: More formal, structured response
    • Claude: More conversational, interactive approach

  2. Initiative

    • GPT-4: Focuses on acknowledgment
    • Claude: Proactively asks for next steps

  3. Tone

    • GPT-4: Professional, systematic
    • Claude: Friendly, collaborative

Recommendations

System Prompt Guidelines

  1. Be Explicit: Clearly state all requirements
  2. Structure Matters: Use numbered lists for multiple tasks
  3. Set Boundaries: Define scope and limitations
  4. Tone Guidance: Specify desired communication style

Model-Specific Adjustments

  • For GPT-4: Can be more conversational
  • For Claude: Can be more structured