ai-learning-guide-with-projects
This chapter teaches domain-specific prompting - embedding industry knowledge, terminology, and workflows into prompts. Students learn to create expert personas and build specialized AI systems for specific domains.
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Cursor IDE rules (legacy)
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View source on GitHubChapter 12: Domain-Specific Prompting - Cursor Rules
Chapter Context
This chapter teaches domain-specific prompting - embedding industry knowledge, terminology, and workflows into prompts. Students learn to create expert personas and build specialized AI systems for specific domains.
Key Concepts
1. Generic vs. Domain-Specific
- Generic: general knowledge, surface-level
- Domain-specific: expert knowledge, deep insights
- Context and terminology matter
- Domain workflows important
2. Expert Personas
- Define expert role and experience
- Specialization areas
- Domain knowledge
- Professional tone
- Industry standards
3. Domain Documentation
- Technical documentation
- API documentation
- User guides
- Domain-specific formats
- Industry standards
4. Domain Code Generation
- Industry patterns
- Best practices
- Domain constraints
- Code standards
- Framework conventions
5. Domain Code Review
- Domain-specific checks
- Industry standards
- Security considerations
- Performance requirements
- Compliance requirements
Important Code Patterns
Domain Expert Persona
domain_expert = """
You are a senior IoT engineer with 15 years of experience in
industrial automation. You specialize in:
- Predictive maintenance systems
- Sensor calibration and diagnostics
- Network protocols (MQTT, CoAP, LoRaWAN)
- Safety-critical systems (IEC 61508)
When analyzing issues, consider:
- Safety implications first
- Production uptime impact
- Cost-effectiveness
- Harsh operating environments
"""
Domain-Specific Analysis
prompt = f"""
As {domain_expert}, analyze this IoT issue:
{problem_description}
Provide analysis focusing on:
- Industry-specific KPIs
- Safety and regulatory implications
- Operational efficiency
- Actionable insights for IoT context
"""
Common Mistakes to Avoid
- Too generic: Always add domain context
- Wrong persona: Match expert to task
- Missing terminology: Use domain language
- No workflows: Include domain processes
- Ignoring standards: Follow industry standards
Integration Points
- Chapters 9-11: Applies prompting strategies to domains
- Chapter 13: Agents can be domain-specific
- Chapter 17: Web apps for domain use cases
Related Chapters
- Chapters 9-11: Prompting strategies (applied to domains)
- Chapter 13: Agents (can be domain-specific)
- Chapter 17: Web applications (domain interfaces)
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