Clean Code Principles for AI-Generated Apps
How to improve the quality of AI-generated code. Naming, structure, error handling, and organization - the essentials.
Why AI code needs cleanup
AI tools generate code that works but isn't organized for humans to maintain. Variable names are unclear, files are too large, logic is duplicated, and error handling is inconsistent. Clean code isn't about perfection - it's about making the code understandable enough that you (or a developer) can confidently make changes.
Naming matters
Good names are the cheapest way to improve code quality. 'data' tells you nothing; 'userSubscriptions' tells you everything. 'handleClick' is generic; 'handleLogout' is clear. When reading code, you should understand what each variable, function, and component does from its name alone.
Small files, single purpose
If a file is longer than 200 lines, it's probably doing too much. Each file should have one clear purpose. A component file renders UI. A hook file manages state logic. A service file handles API calls. If you can't describe what a file does in one sentence, split it.
Error handling
Every external operation (API call, database query, file read) can fail. Good error handling: catches the error, logs it for debugging, and shows the user a helpful message. Bad error handling: lets the app crash, shows a blank screen, or silently swallows the error so bugs go unnoticed.
Consistent patterns
Pick one way to do things and stick with it. One async pattern (async/await, not sometimes .then()). One error handling approach. One naming convention. One file organization structure. Consistency makes code predictable - even mediocre patterns are better when applied consistently.
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