Spaghetti Code in AI-Generated Apps

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.

When working on multiple files, AI doesn't always understand the purpose of files in the architecture. It puts code where it shouldn't be — because technically it "works," but architecturally it's a disaster.

Why AI can't refactor a large codebase

We tried everything. Planning the refactor with one model, executing with another. Doing everything with a single model. Breaking it into smaller refactors. Every time, we ended up with a weird feeling of not understanding what was output during review.

Multiple times we had to discard everything and do it by hand. This isn't a matter of the wrong tool or a bad prompt. AI is simply not capable enough to handle large codebase context to fix a bug, add a feature, or refactor. Sometimes everything is so intricate that only a human mind can process it by decoupling correctly.

One developer we spoke with 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."

Key principle

The bigger the codebase, the more understanding humans should have. A landing page doesn't need human business knowledge, but a real, complex application does. AI cannot replace contextual understanding.

//how to fix it

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 what we do:

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.

Tip

Don't try to fix spaghetti code with more AI prompts. That's like trying to put out a fire with gasoline. You need a human who understands architecture and can make deliberate decisions about what to keep, what to rewrite, and what to throw away.

Spaghetti Code in AI-Generated Apps

The degradation loop: every iteration makes the code worse

Why AI can't refactor a large codebase

How to regain control over spaghetti code

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