A Juejin tutorial teaching beginners to use AI to build a to-do list page recently crossed 10,000 reads. The comments section was flooded with the same question—"I followed the steps, why are there still bugs?" We judge this points to a universally skipped step: acceptance.

Code that runs doesn't mean code that works—and that's exactly where non-engineers using AI to write code most often fall flat.

What This Is

The article offers a five-layer acceptance framework, from shallow to deep:

  • Layer 1: Is the syntax correct? (Are brackets, quotes, and braces paired?)
  • Layer 2: Does the code run? (Are dependencies installed? Do element IDs match?)
  • Layer 3: Is the functional output correct? (This is the most error-prone layer—the code doesn't throw errors but does the wrong thing)
  • Layer 4: Are edge cases and exceptions handled? (Empty inputs, oversized text, rapid clicks)
  • Layer 5: Does it fit the current project? (Is the framework version right? Are new dependencies allowed?)

We noticed the article hammers one phrase repeatedly: "Don't judge by 'looks right.'" That is precisely the most common mistake non-engineers make—equating "runs without errors" with "works correctly."

Industry View

Supporters argue the framework pulls acceptance out of the "engineers-only" zone and into "anyone can use it" territory—meaningful for driving AI-coding adoption. The article's "incomplete-item count" example is telling—AI wrote todos.length instead of filtering for incomplete items. The code ran; the number was wrong.

The dissenting view is worth hearing too. A senior engineer commented on a similar thread: "If acceptance relies on manual checklists, you're just kicking the problem down the road. Company-wide adoption must pair automated testing with type checks (tools like TypeScript that catch errors as you write them)—otherwise, the more AI writes, the more bugs ship."

A more realistic concern: the five-layer check requires time and effort, while AI's whole appeal is speed. Most people will quit at layer three, because layers one and two already "look fine."

Impact on Regular People

Enterprise IT: As more employees use AI to generate internal tools, IT departments may need a new role—not developer, but "business-savvy AI code acceptance reviewer."

Individual careers: Operations, marketing, and admin roles are already using AI to build small tools. Getting to layer-three acceptance at minimum will save massive amounts of "post-launch firefighting" time.

Consumer market: As more products from small teams and even solo developers carry AI fingerprints, consumers evaluating those products may look not just at features, but at the "code acceptance report."