Core Principles
Clear Communication
Write specific, unambiguous prompts that clearly express your intent
- Use concrete examples in your prompts
- Specify the desired output format
- Include relevant context and constraints
Iterative Refinement
Start simple and gradually refine your approach based on results
- Begin with basic prompts and add complexity
- Test different approaches systematically
- Keep track of what works and what doesn't
Validation & Review
Always validate AI-generated content before using it in production
- Review code for logic errors and security issues
- Test generated solutions thoroughly
- Verify factual accuracy of information
Security First
Maintain security best practices when working with AI tools
- Never share sensitive data in prompts
- Keep API keys secure and rotate regularly
- Review generated code for security vulnerabilities
Prompt Engineering Best Practices
Structure Your Prompts
❌ Poor Example
Write code for sorting
✅ Better Example
Write a Python function that sorts a list of integers in ascending order using the quicksort algorithm. Include error handling for empty lists.
Why it's better: Specific language, algorithm, requirements, and edge cases
Provide Context
❌ Poor Example
Fix this bug
✅ Better Example
This React component is not updating when props change. The component should re-render when the 'data' prop updates. Here's the current code: [code]
Why it's better: Clear problem description with relevant code context
Specify Output Format
❌ Poor Example
Explain this API
✅ Better Example
Create documentation for this REST API in Markdown format. Include: endpoint description, parameters, request/response examples, and error codes.
Why it's better: Defined format and required sections
Workflow Integration
Development Workflow
Quality Assurance
Code Quality
- Syntax correctness
- Logic validation
- Performance considerations
Security Review
- Input validation
- Authentication checks
- Data protection
Documentation
- Code comments
- API documentation
- User guides
Common Pitfalls to Avoid
Over-reliance on AI
Using AI for every task without considering if it's the right tool
Solution: Evaluate each task and use AI where it adds genuine value
Insufficient Testing
Deploying AI-generated code without thorough testing
Solution: Always test generated code in isolated environments first
Ignoring Context
Not providing enough context for accurate AI responses
Solution: Include relevant background information and constraints
Security Oversights
Not reviewing generated code for security vulnerabilities
Solution: Implement security reviews as part of your AI workflow