Clean Code in the Age of Copilot: Why Semantics Matter More Than Ever

Abstract Generative AI tools treat your codebase as a prompt; if your context is ambiguous, the output will be hallucinated or buggy. This article demonstrates how enforcing clean code principles — specifically naming, Single Responsibility, and granular unit testing — drastically improves the accuracy and reliability of AI coding assistants. Introduction There is a prevailing misconception that AI coding assistants (like GitHub Copilot, Cursor, or JetBrains AI) render clean code principles obsolete. The argument suggests that if an AI writes the implementation and explains it, human readability matters less.