This week we read a practical**summary on Juejin**: the author used a small Express project to demonstrate the complete AI-assisted code review workflow—5 mandatory checks, 3 refactoring decision conditions, 6 Git commit checklist items. Conclusion: what determines AI coding outcomes is not the model, it's the process.
What this is
The article comes from a task-api project practical series (Day 22–25**),****where the author demonstrates four steps for using AI to do code review: first, hand the AI the requirements, interface contracts (the agreed-upon API formats between frontend and backend), test results, and scope of changes all at once, with the explicit instruction "only list risks, do not modify code"; then run through 5 check items (whether input is validated, whether error codes are distinguishable, whether exceptions are being swallowed, whether fields will be overwritten, whether tests verify real side effects) one by one; only after confirming the code is worth refactoring, let the AI make minimum-scope changes, constrained by**"**keep external behavior unchanged"; finally, use three gates to validate: tests + interface checks + Git diff.
The core judgment is clear: as long as the interface path, status code, error structure, or field name changes, it's no longer "refactoring"—it needs**to go back through** requirements review. This line clearly separates "changing code" from "changing the product."
Industry view
The affirmative side believes this is the correct posture for landing AI coding tools—treat the AI as a "tire**less but boundary-needing junior engineer**", give it explicit check items and validation conditions, which is far more reliable than just saying "help me optimize this code." This**is also why** many teams try AI coding and then give up: the prompts are too loose, and the results are completely uncontrollable.
The opposing view is more realistic: this process works**on** small projects; in large production systems, no one dares to let AI directly modify core code. The cautious principle of "no evidence, don't list as a defect" gets eaten alive by process friction in large teams.**A****stronger critique**: if developers themselves don't know what to review, the AI's risk list only creates anxiety—AI just liberates engineers from "writing code" to "making judgments," it does not eliminate judgment itself.
Impact on regular people
For enterprise IT teams: what determines AI coding effectiveness isn't the model itself, it's how strict the code review process is. Teams with loose processes will only amplify chaos when adopting AI tools; teams with tight processes can save headcount with AI.**That's****the** biggest takeaway for managers.
For individual careers: writing constrained prompts like "do not modify code, only list risks" is becoming a transferable skill for programmers. Compared to "let AI write code," "let AI find problems by rules" has a lower barrier and higher reusability.
For the consumer market**:** no direct short-term impact—the real users of AI coding are developers, not consumers. But if AI can stably produce production-grade code in**the** future,**the** cost structure of enterprise software could shift.