🟢
Low Risk Patterns
Safe starting points for AI adoption with minimal risk exposure
Safe AI Development Practices
These patterns provide a secure foundation for integrating AI coding assistants into your development workflow without compromising security or introducing significant operational risks.
Code Generation
- • Generate boilerplate code and templates
- • Create utility functions and helpers
- • Generate test cases and documentation
- • Build configuration files
Code Review & Analysis
- • Review code for best practices
- • Identify potential bugs and issues
- • Suggest improvements and optimizations
- • Generate code documentation
Learning & Research
- • Explain complex code concepts
- • Research libraries and frameworks
- • Compare implementation approaches
- • Understand legacy codebases
Development Workflows
- • Automate repetitive coding tasks
- • Generate commit messages
- • Create project documentation
- • Build development tooling
Key Safeguards
Always review AI-generated code before committing
Never include sensitive data in prompts
Use version control for all changes
Test generated code thoroughly
Implementation Examples
Utility Function Generation
Use AI to generate common utility functions like date formatters, validation helpers, and data transformers. These are self-contained and easy to test.
Test Case Creation
Generate comprehensive test suites for existing functions, including edge cases and error conditions. Perfect for improving code coverage safely.
Documentation Automation
Create API documentation, README files, and inline code comments. Helps maintain project documentation without risk to functionality.