At first, with precise prompts, it delivers modern, up-to-date code. But when it comes to refactoring and cleaning up, it may remove newer patterns without fully understanding the architecture, reverting the project back to older methods. This leads to increased technical debt, hidden bugs, and a codebase that becomes harder to maintain in the future.
The problem isn’t the AI itself it’s the lack of architectural control and constraints. If you don’t clearly define the framework version, design patterns, architectural rules, and boundaries, the AI tends to oversimplify.
Simply put:
When you build a project with AI, the first version looks impressive modern methods, new patterns, sometimes even over-engineered in a good way.
But when you move on to refactoring, code cleanup, optimization, and reducing complexity, that’s where things start to break. The AI loses the full context of the project. It removes modern dependencies, replaces structured abstractions with more generic (and sometimes outdated) patterns, and oversimplifies critical architectural layers.
The result? The project still works but the architecture has quietly collapsed. And if this continues, even advanced prompting may not be enough to recover it.
So if you’ve received a seemingly solid AI-generated project, you shouldn’t fully delegate maintenance, refactoring, clean coding, optimization, or architectural management to AI. The risk of unintended damage is high.
At the end of the day, you as the developer are responsible for maintaining, refactoring, optimizing, and safeguarding the structure of your project.
This was an experience I gained an interesting and valuable lesson.
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