System Prompt Case Studies

A collection of real system prompt designs from major AI companies. Study these cases to understand industry best practices, then apply them to your own AI Agent development.

---

Case Overview

---

Anthropic Claude

Claude.ai & Claude Code

Core design philosophy: Safe, helpful, honest

Key features:

• RLHF safety mechanisms: Constitutional AI design
• CLAUDE.md configuration: Users can customize agent behavior
• Tool specifications: Detailed Tool Use constraints
• Multi-role switching: Adapts to different scenarios via system prompt

Best suited for:

• Coding assistant development
• Long document processing
• Applications requiring high safety

Typical design pattern:

You are Claude, made by Anthropic...
You can use tools to complete tasks...
NEVER do X without explicit permission...

Deep dive into Claude System Prompts

---

OpenAI GPT

GPT-4o & Agent Mode

Core design philosophy: General-purpose, flexible, rich ecosystem

Key features:

• Function Calling: TypeScript Namespace-style tool definitions
• Message channel system: analysis / commentary / final separation
• Financial activity restrictions: Explicit Allowed / Not Allowed lists
• Safe browsing rules: Defense against Prompt Injection

Best suited for:

• Browser automation
• Complex multi-step tasks
• Applications requiring tool calling

Typical design pattern:

namespace tools {
	type function_name = (_: { param: string }) => any;
}

Deep dive into GPT System Prompts

---

Google Gemini

Gemini CLI & Guided Learning

Core design philosophy: Project conventions first, workflow-driven

Key features:

• Project conventions first: NEVER assume — analyze existing code first
• Five-step workflow: Understand → Plan → Implement → Verify Tests → Verify Standards
• Guided learning: Socratic teaching method
• Self-verification loops: Emphasis on testing and standards checks

Best suited for:

• Code agent development
• Educational AI applications
• Scenarios requiring strict workflows

Typical design pattern:

## Software Engineering Tasks
1. Understand: Think about the user's request...
2. Plan: Build a coherent plan...
3. Implement: Use available tools...
4. Verify (Tests): Run project tests...
5. Verify (Standards): Run linting...

Deep dive into Gemini System Prompts

---

xAI Grok

Grok 3/4 & Persona System

Core design philosophy: Personalization, real-time information, X platform integration

Key features:

• Persona system: Multi-personality role switching
• X platform integration: Native X search and analysis support
• Render components: Dedicated citation and formatting system
• Real-time information: No strict knowledge cutoff date

Available Personas:

• Companion
• Unhinged Comedian
• Loyal Friend
• Homework Helper
• Not a Doctor / Not a Therapist

Best suited for:

• Social media applications
• Personalized chatbots
• Applications requiring real-time information

Typical design pattern:

You are Grok, a [personality] chatbot...

## Style Rules:
- match the user's vulgarity
- always write in lowercase
- use abbreviations like rn ur bc

Deep dive into Grok System Prompts

---

Other AI Products

Perplexity, Kagi, Raycast AI

Three products, three distinct approaches:

Learning value:

• Perplexity: How to design search-oriented AI (re-search on every follow-up)
• Kagi: How to design detailed output format specifications
• Raycast: How to integrate user system preferences (language, timezone, units)

Deep dive into other AI product System Prompts

---

Design Pattern Comparison

From these cases, we can distill several core design patterns:

1. Identity Definition Patterns

2. Tool Definition Patterns

3. Safety Boundary Patterns

---

How to Apply These Cases

1. Choose a Reference Template

Pick the best reference based on your use case:

• Coding assistant → Reference Claude Code, Gemini CLI
• Search application → Reference Perplexity
• Chatbot → Reference Grok Personas
• Browser automation → Reference GPT Agent Mode

2. Reuse Design Patterns

Directly reuse proven design patterns:

SYSTEM_PROMPT = """
# Identity (reference: Claude)
You are [Your Agent Name], a [role] assistant.

# Tools (reference: OpenAI)
You have access to the following tools...

# Constraints (reference: Grok)
## Boundaries (Never Do):
- Never do X without permission
- Never share Y information

# Output (reference: Kagi)
Format your response with proper markdown...
"""

3. Iterate and Optimize

1. Start with a simple version
2. Observe agent behavior
3. Gradually add constraints and examples
4. Test edge cases
5. Refine safety boundaries

---

Further Reading

• System Prompt Design in Practice - 10 reusable design patterns
• Prompt Master Course - Systematic Prompt Engineering training
• AI Agent Development - Building autonomous AI agents

---

These cases all come from real products, showing how top companies approach system prompt design. Study them, then develop your own style.