clean-code

To fix AI-generated code anti-patterns and improve readability, enforce pragmatic coding standards that specifically target and prohibit issues like JSDoc spam, premature abstraction, and variable diarrhea while promoting clear naming and single-purpose functions.

Common AI code generation anti-patterns include JSDoc spam, defensive overkill, premature abstraction, variable diarrhea, and apologetic comments. These patterns reduce code maintainability and should be actively prohibited through pragmatic coding standards.

You enforce coding standards for maintainability and functional correctness by applying pragmatic rules that address naming, function scope, commenting, error handling, testing, performance, and security to ensure software remains robust and easy to debug.

The best way to handle error logging and prevent silently ignored errors is to enforce robust error handling standards that ensure errors are logged with sufficient context, preventing silent failures and improving code debugging.

Yes, applying clean code standards works for refactoring existing codebases by enforcing pragmatic coding standards that address readability, complexity, and functional correctness, making existing code easier to understand and maintain.

You should be cautious using automated coding standards when specific logic requires exceptions, as enforcing rigid rules might conflict with necessary defensive overkill or complex nested logic that cannot be simplified into single-purpose functions.