10 AI Prompt Best Practices Every Developer Should Know
Discover the proven strategies that professional developers use to optimize their AI prompts for better code generation and problem-solving.
As AI tools become integral to software development, knowing how to communicate effectively with these models is a critical skill. Here are ten essential best practices that will transform your AI-assisted development workflow.
1. Specify the Programming Language and Version
Always explicitly state which language and version you're working with. "Write a function in Python 3.11" is much better than "Write a function."
2. Provide Type Information
Include type hints, interfaces, or schemas when relevant. This helps the AI generate more accurate, type-safe code.
3. Define Your Coding Style
Mention your preferred coding conventions: "Use functional programming style," "Follow Google's JavaScript style guide," or "Use async/await instead of promises."
4. Include Error Handling Requirements
Don't forget to specify how you want errors handled. Should the function throw exceptions, return error objects, or use Result types?
5. Request Documentation
Ask for inline comments, JSDoc, or docstrings alongside the code. "Include comprehensive JSDoc comments" ensures your generated code is well-documented.
6. Specify Test Requirements
Request unit tests with your code: "Include Jest test cases covering edge cases and error conditions."
7. Mention Performance Considerations
If performance matters, say so: "Optimize for speed," "Minimize memory usage," or "Use efficient algorithms for large datasets."
8. Define Input and Output Examples
Show concrete examples of expected inputs and outputs. This dramatically improves the accuracy of generated code.
9. Break Complex Tasks into Steps
Instead of asking for an entire feature, break it down: first the data model, then the business logic, then the API endpoints.
10. Iterate and Refine
Use follow-up prompts to refine the output: "Make this more efficient," "Add error handling for network failures," or "Simplify this logic."
Putting It All Together
Here's an example that combines these practices:
"Write a TypeScript function using async/await that fetches user data from an API. Include proper error handling with custom error types, TypeScript interfaces for the response, comprehensive JSDoc comments, and Jest unit tests. The function should handle network errors, timeout after 5 seconds, and validate the response structure. Follow the Airbnb style guide."
Notice how this prompt specifies the language, style, error handling, testing, and constraints all at once.
Final Thoughts
These best practices aren't just about getting code that works—they're about getting production-ready, maintainable, well-documented code that fits seamlessly into your project.