Part 4: Domain Specialization

Modern AI system prompts often include specialized instructions and knowledge for specific domains. This section examines how different AI systems implement domain specialization to enhance their capabilities in particular areas.

4.1 Software Development Specialization

Several AI systems in our analysis include specialized instructions for software development tasks.

Cursor's Code-Centric Specialization

Cursor's system prompt includes extensive domain-specific instructions for code understanding and generation:

When analyzing code:
- First understand the overall structure and purpose of the code
- Identify key components, functions, and data structures
- Consider the programming paradigm and language-specific idioms
- Look for potential issues, inefficiencies, or bugs

When generating code:
- Follow the conventions and style of the existing codebase
- Use appropriate data structures and algorithms for the task
- Consider edge cases and error handling
- Include comments for complex logic
- Ensure the code is readable and maintainable

Source: Cursor system prompt

These specialized instructions enable Cursor to provide more effective assistance for software development tasks.

Devin's Software Engineering Focus

Devin's system prompt is heavily specialized for software engineering:

You are Devin, a software engineer using a real computer operating system.
You have access to a terminal, web browser, and code editor.
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

Source: Devin system prompt

This specialization enables Devin to function effectively as an autonomous software engineer, capable of understanding, planning, and executing complex development tasks.

4.2 Web Development Specialization

Some AI systems include specialized instructions for web development tasks.

Lovable's Web Development Focus

Lovable's system prompt includes detailed domain-specific instructions for web development:

When developing web applications:
- Follow modern web development best practices
- Ensure responsive design for different screen sizes
- Consider accessibility requirements
- Optimize for performance
- Implement proper error handling

For React applications:
- Use functional components and hooks
- Follow the React component lifecycle
- Manage state appropriately
- Implement proper prop validation
- Consider component reusability

Source: Lovable system prompt

This specialization enables Lovable to provide more effective assistance for web development tasks, particularly for React applications.

4.3 Data Analysis Specialization

Some AI systems include specialized instructions for data analysis tasks.

Manus Data API Integration

Manus includes a sophisticated data API module for accessing authoritative data sources:

<datasource_module>
- System is equipped with data API module for accessing authoritative datasources
- Available data APIs and their documentation will be provided as events in the event stream
- Only use data APIs already existing in the event stream; fabricating non-existent APIs is prohibited
- Prioritize using APIs for data retrieval; only use public internet when data APIs cannot meet requirements
- Data API usage costs are covered by the system, no login or authorization needed
- Data APIs must be called through Python code and cannot be used as tools
- Python libraries for data APIs are pre-installed in the environment, ready to use after import
- Save retrieved data to files instead of outputting intermediate results
</datasource_module>

Source: Manus prompt.txt

This specialization enables Manus to access and analyze data from authoritative sources, which is essential for data-intensive tasks.

4.4 Content Creation Specialization

Some AI systems include specialized instructions for content creation tasks.

Manus Writing Rules

Manus includes detailed domain-specific instructions for content creation:

<writing_rules>
- Write content in continuous paragraphs using varied sentence lengths for engaging prose; avoid list formatting
- Use prose and paragraphs by default; only employ lists when explicitly requested by users
- All writing must be highly detailed with a minimum length of several thousand words, unless user explicitly specifies length or format requirements
- When writing based on references, actively cite original text with sources and provide a reference list with URLs at the end
- For lengthy documents, first save each section as separate draft files, then append them sequentially to create the final document
- During final compilation, no content should be reduced or summarized; the final length must exceed the sum of all individual draft files
</writing_rules>

Source: Manus prompt.txt

This specialization enables Manus to create high-quality, detailed content that meets user expectations.

4.5 Deployment Specialization

Some AI systems include specialized instructions for deployment tasks.

Manus Deployment Rules

Manus includes detailed domain-specific instructions for deployment:

<deploy_rules>
- All services can be temporarily accessed externally via expose port tool; static websites and specific applications support permanent deployment
- Users cannot directly access sandbox environment network; expose port tool must be used when providing running services
- Expose port tool returns public proxied domains with port information encoded in prefixes, no additional port specification needed
- Determine public access URLs based on proxied domains, send complete public URLs to users, and emphasize their temporary nature
- For web services, must first test access locally via browser
- When starting services, must listen on 0.0.0.0, avoid binding to specific IP addresses or Host headers to ensure user accessibility
- For deployable websites or applications, ask users if permanent deployment to production environment is needed
</deploy_rules>

Source: Manus prompt.txt

This specialization enables Manus to effectively deploy websites and applications, which is essential for development and demonstration tasks.

4.6 Domain-Specific Knowledge

In addition to specialized instructions, some AI systems include domain-specific knowledge modules.

Manus Knowledge Module

Manus includes a knowledge module that provides domain-specific best practices:

<knowledge_module>
- System is equipped with knowledge and memory module for best practice references
- Task-relevant knowledge will be provided as events in the event stream
- Each knowledge item has its scope and should only be adopted when conditions are met
</knowledge_module>

Source: Manus prompt.txt

This module enables Manus to access and apply domain-specific knowledge and best practices, which enhances its effectiveness across different domains.

4.7 Domain Specialization Techniques

Our analysis reveals several common techniques for implementing domain specialization in AI system prompts:

1. Domain-Specific Instructions

The most common approach is to include detailed instructions for specific domains, as seen in Cursor's code-centric instructions and Lovable's web development guidelines.

2. Specialized Modules

Some systems, like Manus, implement specialized modules for different domains, such as the data API module for data analysis and the knowledge module for best practices.

3. Domain-Specific Terminology

AI system prompts often include domain-specific terminology and concepts, which helps the AI understand and communicate effectively in that domain.

4. Task-Specific Workflows

Many systems include task-specific workflows for common tasks in a domain, such as Devin's debugging workflow and Manus's deployment workflow.

5. Domain-Specific Examples

Some systems include examples of common tasks and solutions in a domain, which helps the AI understand how to approach similar problems.

4.8 Domain Specialization Implications

The domain specialization patterns observed in modern AI system prompts have significant implications for AI system design:

Enhanced Capabilities: Domain specialization enables AI systems to provide more effective assistance for specific tasks
Improved Accuracy: Domain-specific instructions and knowledge help AI systems generate more accurate and appropriate responses
Better User Experience: Specialized AI systems can provide a more tailored and helpful user experience
Increased Efficiency: Domain-specific workflows and examples help AI systems complete tasks more efficiently
Broader Applicability: By combining multiple domains of specialization, AI systems can address a wider range of user needs

By implementing domain specialization, AI system designers can create more capable and effective AI assistants that excel in specific areas.

Key Takeaways

Modern AI system prompts often include domain specialization to enhance capabilities in specific areas
Software development specialization enables AI systems to understand and generate code more effectively
Web development specialization provides tailored assistance for creating web applications
Data analysis specialization enables AI systems to access and analyze data from authoritative sources
Content creation specialization helps AI systems generate high-quality, detailed content
Deployment specialization enables AI systems to effectively deploy websites and applications
Domain-specific knowledge modules provide access to best practices and specialized information

In the next section, we'll explore how AI system prompts implement safety and alignment mechanisms to ensure responsible AI behavior.