Spaghetti Code in AI-Generated Apps
You ask AI to fix a bug — it fixes one thing but breaks two others. Next iteration? Even worse. Learn why refactoring AI code fails and what actually works.
The degradation loop: every iteration makes the code worse
You know the scenario: you ask AI to fix a bug. AI fixes it, but breaks something else in the process. You ask it to fix the new problem. AI fixes that, but breaks two more things. Every iteration makes it worse.
This isn’t an exception — it’s the rule. AI doesn’t understand your app’s architecture as a whole. It sees the files you feed it, but it doesn’t understand why the code is organized a certain way. It puts fixes where they don’t belong. Duplicates logic. Creates dependencies that make no sense.
Across multiple files, it loses track of what each one is for. So it drops code wherever it happens to fit — technically it “works,” but architecturally it’s a disaster.
Why AI can’t refactor a large codebase
Teams try everything. Planning the refactor with one model, executing with another. Doing everything with a single model. Breaking it into smaller refactors. Every time, the review ends the same way: nobody fully understands the code AI produced.
More than once, the only workable move has been to discard what the model wrote and do it by hand. This isn’t a matter of the wrong tool or a bad prompt. AI simply can’t hold enough of a large codebase in context to fix a bug, add a feature, or refactor safely. Sometimes the pieces are so tangled that only a human can untangle them the right way.
One developer who shared his story lost productivity using AI agents in a large codebase — and worse, lost understanding of some parts of the code. He had to reclaim that understanding later, manually. As he put it: “AI slop + hype gave me a good wake-up call to review more deeply what’s output.”
How to regain control over spaghetti code
The solution isn’t “a better prompt” or “a different AI model.” The solution is human analysis and deliberate refactoring. Here’s the approach:
- Code review — a real one. Not a quick glance at the diff, but deep understanding of what every change does and why. If you don’t understand the generated code, don’t accept it.
- Identify root causes. Before you start fixing, understand why the code is in this state. Which architectural decisions led to the current mess?
- Refactor critical parts. Don’t try to refactor everything at once. Find the places where every change triggers a cascade of bugs, and fix those first. This breaks the degradation chain.
- Establish clean architecture patterns. Set clear rules: separation of concerns, unidirectional data flow, clean interfaces between modules. This prevents future mess.
- Reclaim code understanding. If you’ve lost track of what your application does, spend time reviewing and understanding every module. Without this, no fix will last.
Read also
- Why AI apps fail in production — spaghetti code is just one of many problems. See the full picture of what AI leaves in your code.
- Scaling issues in AI-generated apps — messy code doesn’t scale. Learn how to fix it.
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