AI for Coding & Development
Use AI to write, debug, explain, and review code more effectively.
Code Generation Best Practices
AI is exceptionally good at coding — but only when prompted well:
Specify the language and framework: "Write a Python function using pandas..."
Describe inputs and outputs: "Takes a list of dictionaries, returns a sorted DataFrame"
Include edge cases: "Handle empty lists, missing keys, and None values"
Request error handling: "Include try/except blocks and meaningful error messages"
Ask for comments: "Add inline comments explaining the logic"
Never deploy AI-generated code without testing. Always review for security vulnerabilities, especially in web applications.
Debugging with AI
AI is an excellent debugging partner:
"I'm getting this error: [paste error]. Here's my code: [paste code]. Explain what's causing the error and how to fix it."
"This function returns the wrong result for input X. Expected Y but got Z. Here's the code: [paste]. Walk through the logic step by step to find the bug."
For complex bugs, use chain-of-thought: "Debug this step by step. For each line, explain what it does and whether it could cause the issue."
Take a coding task from your current work. Write a detailed prompt with language, framework, inputs, outputs, edge cases, and error handling requirements. Compare the output to code you'd write manually.
- ✓Always specify language, framework, inputs, outputs, and edge cases
- ✓Never deploy AI code without testing and security review
- ✓AI excels at debugging — paste errors and code together
- ✓Use chain-of-thought for complex debugging tasks