There's a pattern I keep watching play out, and it almost always goes the same way.
Someone spins up a project using an LLM. The first few iterations are magic. Features land fast, the code runs, the demo looks great. Everyone's excited.
Then you hit iteration 15. Or 25. And things start getting weird.
A change in the auth module quietly breaks something in the payment flow. A refactor of the LLM suggested works perfectly for the file it touched, but creates a naming convention that conflicts with everything else in the project. Tests pass because they were generated against the same assumptions the code was, so they're both wrong in the same direction.
The core issue is that LLMs optimize for the immediate problem. They're incredible at taking a focused prompt and producing code that solves it. But they don't carry a mental model of your full codebase architecture. They don't know that the helper function they just created duplicates one that already exists three folders away. They don't remember that you made a deliberate decision two weeks ago about how the state is managed, and the code they just wrote violates that decision.
This isn't really an AI problem. It's the same problem that happens with any approach that prioritizes speed over structure. The difference is that AI makes it possible to accumulate tech debt at a rate that used to be physically impossible. You can generate a thousand lines a day, and every single one of them can be locally correct while the overall system slowly becomes unmaintainable.
What I've seen work is pretty boring, honestly. Treat LLM output the same way you'd treat code from a new contractor who's talented but has never seen your codebase. Review it in context, not in isolation. Keep architectural decisions documented somewhere that the human team references regularly. Run static analysis and complexity checks on every commit, not just linting but actual maintainability metrics, so you catch the structural rot before it compounds.
The teams that stay productive with AI long term aren't the ones generating the most code. They're the ones who figured out that the generation is the easy part and the integration is the actual work.
How are you keeping your codebase healthy as AI-generated code becomes a bigger percentage of what ships?
Top comments (0)