AI Can Explain Good Code Standards But Often Fails to Actually Produce Them

A developer on DEV Community has highlighted a recurring gap between how AI models describe best coding practices and the code they actually generate. When asked conceptual questions, AI tools tend to give textbook-perfect answers, but when tasked with writing real components, they often produce bloated, hard-to-read output spanning thousands of lines. The author uses the example of AI-generated React components that are visually functional yet structurally poor, with deeply nested logic and excessively long lines. While fixes like better prompting, fine-tuning, or added context can help, the author questions why such corrections are necessary if the model already understands quality standards. The piece raises an open question about whether the disconnect lies in AI reward systems, training objectives, or something else not yet fully understood.