Conclusion

This technical guide has explored the patterns, observations, and unusual elements found in leaked system prompts from various AI tools, including Cursor, Devin, Lovable, and Manus. Through this analysis, we've gained valuable insights into the architecture, operational frameworks, communication models, domain specialization, safety mechanisms, and implementation techniques used in modern AI system prompts.

Key Takeaways

Our analysis reveals several key takeaways for technical professionals interested in prompt engineering:

1. Structural Evolution

Modern AI system prompts have evolved from simple instructions to sophisticated architectural frameworks. The most advanced prompts use hierarchical structures, modular design, and semantic markup to organize complex instructions and enable more capable AI systems.

As seen in Manus's system prompt:

<agent_loop>
You are operating in an agent loop, iteratively completing tasks through these steps:
1. Analyze Events: Understand user needs and current state through event stream...
2. Select Tools: Choose next tool call based on current state...
3. Wait for Execution: Selected tool action will be executed...
4. Iterate: Choose only one tool call per iteration...
5. Submit Results: Send results to user via message tools...
6. Enter Standby: Enter idle state when all tasks are completed...
</agent_loop>

2. Function-Based Agency

The most sophisticated system prompts use function-based agency to enable structured interaction with external systems. This approach allows AI systems to perform complex tasks by breaking them down into discrete function calls with well-defined parameters and expected behaviors.

Manus's function definitions exemplify this approach:

<function>{"description": "Execute commands in a specified shell session.\n\nRecommended scenarios:\n- When running code\n- When installing packages\n- When copying, moving, or deleting files\n- When user explicitly requests to wake up sandbox environment, boot up, or check status\n\nBest practices:\n- Use absolute paths when specifying file locations\n- Verify command safety before execution\n- Prepare backups or rollback plans when necessary\n- Use uptime command when requested to wake up sandbox environment or check status", "name": "shell_exec", "parameters": {"properties": {"command": {"description": "Shell command to execute", "type": "string"}, "exec_dir": {"description": "Working directory for command execution (must use absolute path)", "type": "string"}, "id": {"description": "Unique identifier of the target shell session; automatically creates new session if not exists", "type": "string"}}, "required": ["id", "exec_dir", "command"], "type": "object"}}

3. Operational Frameworks

Advanced system prompts implement sophisticated operational frameworks that guide AI behavior across different contexts and tasks. These frameworks include agent loops, decision trees, planning mechanisms, and error recovery strategies.

Devin's problem-solving framework illustrates this approach:

When solving problems:
1. Break down complex tasks into smaller steps
2. Plan your approach before implementation
3. Test your solutions thoroughly
4. Document your work clearly

For debugging:
- Read error messages carefully
- Check logs for relevant information
- Use print statements or debuggers to trace execution
- Test hypotheses systematically
- Fix one issue at a time

4. Safety and Alignment

Modern system prompts implement various safety and alignment mechanisms to ensure responsible AI behavior. These include behavioral constraints, content moderation, tool use restrictions, privacy protection, and transparency requirements.

Manus's critical safety instructions demonstrate this focus:

<CRITICAL>If the user requests you to create an account on communications, chat, entertainment or community platforms (such as gmail, reddit, tiktok, meta, 4chan, discord) apologize to the user and state that you cannot do this. You can visit these websites but should not assist in creating accounts. If the user requests you to generate and post comments or reactions to social media or news aggregators or websites or send SMS messages etc, apologize to the user and state that you cannot do this. When you see a screen with a human verification system like captcha or ReCaptcha, stop and ask for direction from the user on how to proceed. When you see a screen with terms of service agreement, stop and ask for direction from the user on how to proceed. Do not click any text box which say "I'm not a robot". You may visit domain and web hosting websites but do not purchase or register for web domains. Do not access, scrape, or collect voter registration data, election infrastructure websites, government databases, or personal information of voters or election officials.</CRITICAL>

5. Comparative Effectiveness

Our analysis reveals that different AI systems employ distinct prompting styles, philosophies, and methodologies that reflect their intended use cases and design goals. Based on our evaluation, Manus demonstrates the most sophisticated prompt engineering techniques (9.5/10), followed by Devin (8.5/10), Cursor (8.0/10), and Lovable (7.5/10).

Manus's modular agent framework, with its comprehensive XML-style markup, function-based agency, and event stream processing, represents the current state of the art in prompt engineering. This approach enables Manus to handle a wide range of tasks with high reliability and effectiveness.

Future Directions

Based on our analysis, we can identify several promising directions for the future evolution of AI system prompts:

1. Enhanced Modularity

Future system prompts will likely feature even greater modularity, with more specialized components that can be combined and reconfigured for different tasks and domains. This approach will enable more flexible and adaptable AI systems.

2. Standardized Markup Languages

As system prompts become more complex, we may see the emergence of standardized markup languages specifically designed for prompt engineering. These languages would provide consistent ways to structure instructions, define functions, and specify operational frameworks.

3. Advanced Safety Mechanisms

Future system prompts will likely incorporate more sophisticated safety mechanisms, including formal verification of AI behavior, dynamic constraint adjustment based on context, and more nuanced approaches to content moderation.

4. Context Management

As AI systems handle increasingly complex tasks, system prompts will need to implement more sophisticated context management mechanisms to maintain coherence and consistency across interactions.

5. Multi-Agent Coordination

Future system prompts may enable coordination between multiple specialized agents, each with its own prompt and capabilities, to tackle complex tasks that require diverse skills and knowledge.

Conclusion

The leaked system prompts analyzed in this guide provide valuable insights into the current state of the art in prompt engineering. By understanding the patterns, techniques, and approaches used in these prompts, technical professionals can develop more effective prompts for their own AI applications.

The evolution from simple instructions to sophisticated architectural frameworks represents a significant advancement in AI system design. As AI capabilities continue to grow, we can expect system prompts to become even more sophisticated, with greater modularity, standardization, safety mechanisms, and coordination capabilities.

For technical professionals interested in prompt engineering, this guide serves as a starting point for understanding the key concepts, patterns, and techniques used in modern AI system prompts. By applying these insights to their own work, they can create more capable, reliable, and aligned AI systems.