AI-generated code fails not because it's sloppy but because it's convincing: it matches your naming conventions, passes your tests, and still misunderstands why your system works the way it does.
The Details:
Large language models excel at pattern completion, and most coding is pattern completion. Function signatures, error handlers, query structures all show up constantly in training data, so the output fits the shape of your codebase without knowing the history behind that shape. A table might be append-only because of an incident three years ago; the model has no way to know that.
Passing tests only confirms the code does what the tests check, not that it understands the system it just joined. A fifty-line diff that resembles surrounding code gets merged under normal time pressure, then gets copied and extended by other engineers for months. By the time the mismatch surfaces in production, it is load-bearing and the original error is buried under layers of code that trusted it.
The fix is to interrogate intent before accepting output, the way you would question a junior engineer: what assumption is being made about the caller, what happens on empty input, why this approach instead of another. If the AI cannot name the invariant it is preserving, that is the warning sign, not the size of the diff or the color of the test suite.
Two habits make this sustainable under deadline pressure instead of collapsing the moment one appears. Keep a short file per service listing non-obvious landmines, like a queue that assumes idempotent consumers or an endpoint called only by a client that never retries. And narrate intent in two sentences before generating code, so the model solves your actual problem instead of the nearest matching pattern.
Bottom Line: The real skill in AI-assisted engineering isn't writing code faster; it's refusing to stop asking why the code is right.
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