Intermediate 15 min read

AI Coding Best Practices

The difference between AI-assisted developers who ship fast and those who fight their tools comes down to workflow. Here's what the best practitioners do differently.

The Right Mindset

AI coding tools are collaborators, not replacements. The best results come from a partnership where you provide direction, context, and judgment while the AI handles implementation details.

Key Principle: You are the architect. AI is the builder. You decide what to build and why. AI helps figure out how.

Before You Start Coding

Plan Before You Prompt

Spend 5 minutes thinking before you type. What are you building? What are the requirements? What edge cases exist? Clear thinking leads to clear prompts.

Break Large Tasks into Small Ones

Never ask AI to build an entire application at once. Break it into features. Break features into components. Small, focused requests get better results.

Asking for Everything at Once

"Build me a full e-commerce site with user auth, product catalog, shopping cart, payment processing, and admin dashboard." This will produce incomplete, inconsistent code.

During Development

Always Review AI Output

Never blindly accept AI-generated code. At minimum:

  • Read the code. Even if you don't understand every line, look for obvious issues.
  • Test it. Run the code. Click the buttons. Enter edge case inputs.
  • Question assumptions. Did the AI make choices you didn't ask for?

Security Warning: AI can introduce security vulnerabilities. Watch for hardcoded credentials, SQL injection, XSS vulnerabilities, and exposed API keys. When in doubt, ask the AI to review its own code for security issues.

Work Iteratively

Build in Layers

Start with the simplest version that works. Add complexity incrementally. Each layer should be tested before adding the next.

Good progression for a new feature:

  1. Basic functionality (happy path only)
  2. Error handling
  3. Edge cases
  4. Performance optimization
  5. Polish and UX improvements

Provide Specific Feedback

When something isn't right, be specific about what's wrong and what you want instead.

Vague Feedback

"This doesn't work" or "Try again" gives the AI nothing to work with. It will often repeat the same mistake.

Specific Feedback

"The form submits but doesn't validate email format. Add validation that checks for @ symbol and domain, and show an error message below the input field when invalid."

Maintaining Code Quality

Keep Code Consistent

AI might use different patterns across your codebase. Enforce consistency by:

  • Providing examples of existing code patterns
  • Specifying naming conventions explicitly
  • Using linters and formatters
  • Reviewing for consistency before committing

Don't Over-Engineer

AI loves to add features you didn't ask for. It might add:

  • Unnecessary abstraction layers
  • Features "in case you need them later"
  • Complex error handling for impossible scenarios
  • Excessive configuration options

Keep It Simple

If you didn't ask for it and don't need it now, remove it. Simple code is easier to understand, test, and maintain.

Understand Your Code

Even if AI wrote it, you own it. You should be able to:

  • Explain what each major function does
  • Debug issues without AI assistance
  • Make small changes manually
  • Explain the architecture to someone else

Danger Zone: If you can't explain what your code does, you can't maintain it. Take time to understand AI-generated code before shipping it.

Common Pitfalls to Avoid

Trusting Without Verifying

AI confidently generates incorrect code. It can hallucinate APIs, invent functions that don't exist, and create subtle bugs. Always verify.

Prompt Loops

Going back and forth with AI without making progress. If three attempts don't work, step back. Rethink your approach. Provide more context or examples.

Skipping Testing

"It looks right" isn't testing. Run the code. Test edge cases. Try to break it. AI-generated code often has subtle bugs in edge cases.

Copy-Paste Without Context

Copying code from one AI session to another without explaining the context. Each session starts fresh—provide the context the AI needs.

Workflow Tips

Use Version Control

Commit frequently when working with AI. If an AI suggestion breaks something, you can easily revert. Good commit points:

  • Before starting a new feature
  • After each working increment
  • Before asking AI to make major changes

Keep Context Fresh

Long conversations accumulate context that can confuse the AI. When things get messy:

  • Start a new conversation
  • Provide a fresh summary of what you're building
  • Reference the current state of your code

Document Decisions

AI won't remember why you made certain choices. Document important decisions in:

  • Code comments for non-obvious logic
  • README files for setup and architecture
  • Commit messages for reasoning behind changes

Pre-Commit Checklist

Before Committing AI-Generated Code

  • I've read and understand the code
  • I've tested the happy path
  • I've tested edge cases
  • I've checked for security vulnerabilities
  • The code follows project conventions
  • No unnecessary complexity was added
  • No hardcoded values that should be configurable
  • Error handling is appropriate
  • The code is properly formatted
  • I can explain this code to someone else

Growing as an AI-Assisted Developer

The best AI-assisted developers continuously improve by:

  1. Learning fundamentals. Understanding programming concepts makes you better at directing AI.
  2. Reading AI output. Treat generated code as learning material. Ask the AI to explain patterns you don't understand.
  3. Experimenting. Try different prompting techniques. Note what works and what doesn't.
  4. Building more. Experience is the best teacher. Build lots of small projects.

Remember: AI tools are evolving rapidly. Stay curious, keep experimenting, and adapt your workflow as tools improve. The developers who thrive will be those who learn to collaborate effectively with AI.