Why AI-generated React diffs are risky in a specific way
Most AI-written pull requests fail in places that look minor in code review but are expensive in production. The JSX looks plausible, the naming is good enough, and the component tree often compiles. The bugs usually sit in state ownership, effect timing, cleanup, and assumptions about when data changes.
That means a normal style-focused review is too shallow. If your team is letting AI draft UI work, the review checklist has to be more behavioral. You are not asking whether the diff looks clean. You are asking whether the component will still behave when users navigate quickly, refetch data, reopen the screen, or interrupt an async flow.
The checklist that catches most React-specific mistakes
Run every AI-assisted React pull request through the same narrow list. If a reviewer cannot answer these questions clearly, the code is not ready yet.
- State ownership: is the source of truth in the right component, or did the AI duplicate state to make the code easier to generate?
- Effect intent: does each effect synchronize with an external system, or is it being used to patch render logic that belongs elsewhere?
- Dependency accuracy: were dependencies removed to silence the linter instead of fixing stale reads?
- Async cleanup: if a request resolves late, will it overwrite newer UI state or update after unmount?
- Identity stability: are keys, callbacks, and derived ids stable enough for lists, forms, and transitions?
- Boundary correctness: if this code is in a Next.js app, are server and client responsibilities still separated cleanly?
- Accessibility: did the generated form or interactive UI preserve labels, button semantics, and keyboard behavior?
What reviewers should ask in the pull request
The fastest useful review comments are not generic. Ask what changed in user behavior, what assumption the new effect makes, and what test proves the component survives interruption. Those questions force the author to explain behavior instead of defending syntax.
A good comment sounds like this: What keeps this request from setting stale data if the route param changes twice before it resolves? Another good one: Why is this stored in local state instead of derived from props? These are small questions, but they expose large weaknesses quickly.
How to operationalize the checklist
Keep the checklist short enough that senior reviewers actually use it. Put it in the pull request template or your team handbook, then tie it to one required proof: at least one behavior-level test for the risky path in the diff.
If your team wants a simpler process, focus on three mandatory checks first: effect intent, async cleanup, and state ownership. That small filter catches a surprising share of AI-generated regressions.