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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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About this skill
📐

.cursorrules

Cursor IDE rules (legacy)

Quality Score

58/100

Category

Automation

Supported Platforms

Cursor
Zed

Chapter 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

  1. Too generic: Always add domain context
  2. Wrong persona: Match expert to task
  3. Missing terminology: Use domain language
  4. No workflows: Include domain processes
  5. 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)

Related Skills

View on GitHub
GitHub Stars0
CategoryAutomation
UpdatedNaNy ago
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Security Score

68/100

Audited on Invalid Date

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