The danger of unexamined AI-generated logic

Accepting LLM outputs without deep scrutiny creates a technical debt loop that most teams aren't prepared to manage. I recently fell into this trap while working on a Swift project—moving fast, prompting, and letting the generator "fix" things until the codebase felt like a black box. It looked functional, but the underlying architecture was essentially a pile of junk shoved under a rug.

To test the integrity of my own workflow, I ran a destructive audit:

1. I manually deleted blocks of code the AI had inserted to see if I could predict the system failure.
2. I compared my mental model of the edge cases against the actual logic provided by the model.
3. I stripped out the "defensive" fluff that the AI adds by default.

The results were a reality check. I realized that much of what I had "accepted" was redundant logic that served no purpose in my specific environment, yet I had been too passive to notice. When I deleted a function I assumed was useless, the entire user flow collapsed because I hadn't accounted for a nil value the AI was silently managing.

Ultimately, I purged 200 lines of code. The project is leaner and more performant now, but the real win was regaining ownership.

We need to shift our team culture from "it compiles, so it's fine" to a more rigorous standard of verification. An LLM isn't just a text generator; it is a decision-making engine. If you aren't auditing those decisions, you aren't an engineer—you're just a supervisor watching a black box run.

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