AI Writes Well, but It Can Also 'Be Confidently Wrong'
AI writes code fast and smoothly, but it has a trait: it's wrong with confidence. What it produces looks tidy, well-commented, and seems to run fine—yet inside may hide an unhandled edge case or an unchecked permission. You simply can't tell right from wrong just because "it looks polished."
So you can't let AI output ship directly. Put a gate between it and "actually taking effect," dedicated to one question: is this thing really correct, really safe? That gate is automated tests + human verification.
Two Lines of Defense, Each Covering a Stretch
Automated tests are like the QC machine on a factory line: feed in all kinds of inputs and automatically check whether the output is right. They're tireless, repeatable, and re-run the whole suite with one code change, best at catching "logic computed wrong, edge case unhandled, fixing A broke B" kinds of problems.
Human review is like the master craftsman's final look: they understand intent and context, catching problems the machine can't test—"this design itself has a security risk," "this runs but the permission is too broad," "this isn't what the requirement wanted at all."
Combo: Machine First, Then Human
In practice you stack the two: after AI produces code, first run the automated tests—quickly blocking a large batch of obvious logic and regression errors; then have a human review the parts the machine can't judge—whether the design is sound, whether permissions are too broad, whether there's a security risk.
Order matters: the machine first saves labor (filtering out low-level errors), the human after guards the critical (holding the judgments the machine can't make). Only after both lines pass is AI output truly "insured."
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Judge: AI-written code that looks tidy, is well-commented, and runs without errors must be correct and can be shipped directly.
答案：No
"Looks correct" isn't "is correct"; AI often "errs with confidence," so its output needs tests and human verification as a safety net and can't be shipped directly.
Why does AI's code output need tests and human verification as a safety net?
答案：Because AI may "err with confidence"—a tidy surface hides edge or security issues, and you can't judge correctness just from looks
AI output may look polished while hiding errors, so you must use tests and human verification as a gate to confirm it's actually correct and safe.
Which kind of problem is usually better caught by **automated tests** than by a human?
答案：Whether changing module A broke module B (a regression error)
Automated tests are repeatable and tireless, best at catching logic errors and regressions; judging design, intent, and permission scope relies more on human review.
Judge: automated tests are good at repeatedly catching logic and regression errors, while human review is better at judging design intent, context, and whether permissions are reasonable—their strengths differ.
答案：Yes
Tests are good at repeatably checking logic/regressions, humans at understanding intent and context—the two complement each other, so stack them.
AI wrote you a "deduct funds and send a confirmation SMS" feature—which safety-net combo is most reasonable?
答案：First run automated tests to block logic/regression errors, then have a human review permissions and security design, ship only if both pass
A critical feature like deducting funds should be doubly insured "machine first, then human": tests filter low-level errors, humans guard security and design, ship only when all pass.
The recommended safety-net flow chant for AI output: AI writes → tests run → ____ → then ship. (Fill in the middle step.)
答案：human reviews
The full order is "AI writes → tests run → human reviews → then ship": the machine first to save labor, the human after to guard the critical.