跪拜 Guibai
← All articles
Backend

When AI Writes the Code, the Human's Job Is Scope, Judgment, and Verification

By 回家路上绕了弯 ·
Read original on juejin.cn ↗ Google Translate ↗ Alt translation

Faster code generation raises the cost of ambiguous requirements and unverified assumptions: a vague goal becomes a sprawling codebase, and AI-written tests can pass while enforcing the wrong business rule. The developer who can't define scope, verify with independent evidence, and retain system understanding ends up owning a delivery they can't explain or troubleshoot.

Summary

AI coding accelerates code generation, but it also makes human judgment the critical factor. The developer's first task is turning a vague request into a precise, implementable requirement: which orders can be cancelled, whether refunds are needed, when inventory is released. Without that clarification, AI's faster output produces larger rework. Next, AI needs trustworthy context: existing pagination logic, module ownership, calling interfaces, team conventions, and historical pitfalls. When documentation and code disagree, someone must decide which one reflects current business, which pushes developers to externalize tacit knowledge.

Scoping and verification become central. The developer decides what must change now, what can be touched, what to leave for later, and when to stop; otherwise AI will happily refactor adjacent code and expand review burden. When choosing between implementations, the human weighs cost, complexity, stability, and team capability. Verification is the trap: AI-generated tests can pass while sharing the same wrong understanding of a business rule, so acceptance criteria, interface conventions, and observable runtime results are the only independent baselines. Using a second AI for review adds a perspective, but the human still checks the evidence.

Finally, developers must retain a working understanding of the system: why data is in a certain state, how retries and rollbacks behave. And each delivery should produce reusable artifacts, whether a design note, a checklist, or an automated tool, so the next collaboration starts from a better baseline. The result is a shift from writing lines to owning the judgment and responsibility for each delivery.

Takeaways
— The first human job is to turn a vague request into an explicit requirement and decide what outcome is acceptable.
— AI needs trustworthy context: existing implementations, module responsibilities, interface callers, team conventions, and historical pitfalls; conflicting docs and code need a human to resolve.
— Scoping is essential: decide what must change now, what may be touched, what to defer, and the conditions for stopping; otherwise AI expands the change and review burden.
— When choosing between implementations, the decision depends on project stage, cost, complexity, stability, and team capability; there is no context-free best answer.
— Verification must use independent baselines: AI-generated tests can pass while sharing the same misunderstanding of a business rule, so check against acceptance criteria, interface conventions, and observable runtime results.
— Developers must retain understanding of key paths, data flow, external dependencies, and failure handling, even if they didn't write the code.
— Each delivery should produce reusable artifacts (design rationale, checklists, automation) so future work starts from a better baseline.
Conclusions

AI-generated tests can pass while enforcing the wrong business rule because the AI's misunderstanding propagates into its test cases, making test green an unreliable signal; independent acceptance criteria and observable behavior become the real safety net.

The article's seven stages amount to a shift from writing code to commissioning and reviewing code, which changes the skill profile for developers: requirement elicitation, scoping, and evidence-based verification matter more than raw syntax fluency.

AI coding pushes teams to externalize tacit knowledge: conventions that used to live in developers' heads must be written down and kept current, or AI will produce divergent patterns that add maintenance cost.

Concepts & terms
AI review
Using a second AI to review code or changes made by the first AI. It adds a different perspective but still requires human verification of the issues it raises and the evidence it provides.
Source: juejin.cn ↗ Google Translate ↗ Backup ↗