Prompt Engineering Mastery Guide

You are a world-class prompt engineering instructor. Create a comprehensive mastery guide:

## PROMPT ENGINEERING CURRICULUM

1. **Foundation Principles**
   - Context setting techniques
   - Instruction clarity optimization
   - Output format specification
   - Constraint implementation
   - Error handling strategies

2. **Advanced Techniques**
   - Few-shot learning implementation
   - Chain-of-thought prompting
   - Tree-of-thought structures
   - Role-based prompting
   - Recursive refinement
   - Meta-prompting strategies

3. **Model-Specific Optimization**
   - GPT-4: Maximum context utilization
   - Claude: Constitutional AI alignment
   - Gemini: Multimodal integration
   - LLaMA: Open-source advantages

4. **Testing & Iteration**
   - A/B testing frameworks
   - Performance metrics
   - Edge case identification
   - Robustness testing
   - Version control systems

5. **Library Development**
   - Prompt categorization
   - Reusability patterns
   - Documentation standards
   - Sharing best practices
   - Community contribution

Create a learning path for: [Beginner/Intermediate/Advanced]
Focus area: [General/Specific domain]

Level: Intermediate developer
Focus: Building SaaS applications
Goal: Create reusable prompt templates for common coding tasks

Prompt: "Let's build a microservice architecture step by step:
1. First, analyze the requirements: [REQUIREMENTS]
2. Design the service boundaries
3. Define API contracts
4. Plan data flow
5. Consider scalability needs
6. Generate implementation code"

Task: Generate API endpoints

Example 1:
Input: User management
Output:
- GET /api/users
- POST /api/users
- GET /api/users/{id}
- PUT /api/users/{id}
- DELETE /api/users/{id}

Example 2:
Input: Product catalog
Output:
[Similar structure]

Now generate for: [YOUR_RESOURCE]

PROMPT_TEMPLATE = """
You are a database architect specializing in SaaS applications.

Context:
- Application type: {app_type}
- Expected scale: {user_count} users
- Key features: {features}

Generate:
1. Optimized PostgreSQL schema
2. Indexes for common queries
3. Partition strategy if needed
4. Migration script
5. Sample queries for main operations

Consider: Multi-tenancy, GDPR compliance, performance
"""

PROMPT_TEMPLATE:
  role: "API documentation specialist"
  task: "Generate OpenAPI 3.0 specification"
  inputs:
    - service_name
    - endpoints_list
    - auth_method
  outputs:
    - full_openapi_spec
    - example_requests
    - error_responses
    - rate_limiting_info

test_cases = [
    {
        "input": "Basic CRUD API",
        "expected_elements": ["endpoints", "status_codes", "validation"],
        "quality_score": 0.9
    },
    # More test cases...
]

SaaS-Prompt-Library/
├── Backend/
│   ├── api-design/
│   ├── database/
│   ├── authentication/
│   └── microservices/
├── Frontend/
│   ├── component-generation/
│   ├── state-management/
│   └── ui-patterns/
├── DevOps/
│   ├── ci-cd/
│   ├── monitoring/
│   └── deployment/
└── Testing/
    ├── unit-tests/
    ├── integration/
    └── e2e/