Skip to main content
AI
7 min read
January 4, 2026

AI-Assisted Code Reviews: What Changes in Your PR Process

The Code Review Is Dead. Long Live the Code Review.

Segev Sinay

Segev Sinay

Frontend Architect

Share:

The Code Review Is Dead. Long Live the Code Review.

Code review has been the backbone of software quality for decades. A human reads another human's code, catches mistakes, shares knowledge, and enforces standards. It works. It is also slow, inconsistent, and scales poorly.

AI-assisted code review does not replace this process. But it fundamentally changes what human reviewers should focus on. And if you do not adapt your PR process to account for this shift, you are either wasting AI or wasting your reviewers' time.

I have been refining the PR process for teams that use AI code review tools for the past year. Here is what I have learned about what changes — and what must stay human.

What AI Reviews Well

Let me be specific about where AI code review delivers consistent value in frontend projects:

Style and convention adherence. AI catches every missing semicolon, every inconsistent naming convention, every import order violation. It does not get tired. It does not have "good days" and "bad days." It applies your rules uniformly across every PR.

This is not trivial. In my experience, 30-40% of human code review comments are about style and convention. Offloading this to AI means human reviewers can focus on things that actually require human judgment.

Type safety gaps. AI is excellent at spotting places where TypeScript types are too loose, where any has crept in, where a type assertion hides a potential runtime error. It can trace type flows across files more reliably than most human reviewers.

// AI catches this pattern reliably
const data = response.data as UserProfile; // Unsafe assertion
// Suggests:
const data = userProfileSchema.parse(response.data); // Runtime validation

Known vulnerability patterns. SQL injection, XSS via dangerouslySetInnerHTML, hardcoded secrets, insecure dependencies — AI catches these patterns with near-perfect recall. It has been trained on millions of vulnerability reports and knows what to look for.

Performance anti-patterns. Unnecessary re-renders, missing memoization, bundle-bloating imports, synchronous operations that should be async — AI flags these consistently.

// AI flags this re-render risk
function ProductList({ category }: Props) {
  // New array reference on every render — causes child re-renders
  const filtered = products.filter(p => p.category === category);

  return filtered.map(p => <ProductCard key={p.id} product={p} />);
}

// AI suggests
function ProductList({ category }: Props) {
  const filtered = useMemo(
    () => products.filter(p => p.category === category),
    [products, category]
  );

  return filtered.map(p => <ProductCard key={p.id} product={p} />);
}

Accessibility issues. Missing ARIA attributes, improper heading hierarchy, non-interactive elements with click handlers, missing keyboard support — AI catches the mechanical accessibility issues reliably.

What AI Reviews Poorly

Here is where I see teams over-trust AI review:

Architectural fit. AI can tell you if a component is well-written. It cannot tell you if that component should exist at all. "Should this be a separate component or part of the parent?" "Does this duplicate functionality in another module?" "Does this approach scale for our roadmap?" These are architectural questions that require context AI does not have.

Business logic correctness. AI can verify that code is syntactically correct and type-safe. It cannot verify that the business logic matches what the product requirements actually need. A perfectly typed function that calculates the wrong discount is still wrong.

User experience implications. "This loading state will feel jarring." "This animation timing will feel sluggish on mobile." "Users will miss this CTA because it is below the fold on common screen sizes." These are human judgments.

Team context. "We tried this approach last quarter and it caused problems because..." "This conflicts with the migration we are planning next month." "The API team told us this endpoint is being deprecated." AI has no access to your team's history, plans, or tribal knowledge.

Subtle bugs in complex state. Race conditions, stale closure captures, incorrect dependency arrays in useEffect — AI catches the obvious cases but misses the subtle ones that depend on understanding the full interaction flow.

The Restructured PR Process

Based on these strengths and weaknesses, here is the PR process I now recommend for frontend teams:

Phase 1: AI Pre-Review (Automated, Before Human Review)

The AI review runs automatically when a PR is opened. It checks:

  • [ ] Style guide compliance
  • [ ] TypeScript strict mode compliance
  • [ ] Import order and structure
  • [ ] Known security patterns
  • [ ] Performance anti-patterns
  • [ ] Accessibility basics
  • [ ] Test coverage thresholds
  • [ ] Bundle size impact

The author addresses AI feedback before requesting human review. This is critical — it means human reviewers never see style nits or mechanical issues. Their time is spent on substantive review.

Phase 2: Author Self-Review with AI Context

Before requesting human review, the author does a self-review informed by the AI feedback. This step includes:

  1. Verify that AI suggestions were correctly applied (AI is not always right)
  2. Add context comments on decisions that might look wrong to a reviewer
  3. Flag areas where you are uncertain and want specific human feedback
  4. Write a clear PR description explaining the "why" not just the "what"

Phase 3: Human Architectural Review

The human reviewer now focuses on:

  • Does this change fit the architecture? Is the component in the right place? Does it follow established patterns? Does it introduce unnecessary coupling?
  • Is the abstraction level correct? Is this too abstract? Not abstract enough? Will this scale?
  • Is the business logic correct? Does this implement the requirement accurately? Are edge cases handled?
  • Are there UX implications? How will this feel to use? Are loading states appropriate? Is the error handling user-friendly?
  • Is this the right approach? Could this be simpler? Is there existing code that does something similar?

Phase 4: Collaborative Resolution

When the reviewer has feedback, the resolution process may involve AI:

  • Mechanical refactoring (renaming, restructuring) can be AI-assisted
  • Architectural changes require human-to-human discussion
  • Complex bug fixes should be human-driven, AI-verified

The PR Template Update

I recommend updating your PR template to reflect AI-assisted review:

## What this PR does
[One paragraph explaining the change and why]

## AI Review Status
- [x] AI pre-review passed
- [x] AI suggestions addressed
- [ ] Any AI suggestions intentionally ignored? (explain below)

## Areas needing human review
- [ ] Architectural fit — [specific concern]
- [ ] Business logic — [specific area]
- [ ] UX implications — [specific interaction]
- [ ] Performance — [specific concern]

## Testing
- [x] Unit tests added/updated
- [x] AI-generated contract tests pass
- [ ] Manual testing done for [specific scenarios]

## Screenshots / Recordings
[If UI changes, include before/after]

Metrics That Change

When you adopt AI-assisted review, your metrics should shift:

Time to first review comment should decrease dramatically. AI reviews in seconds, not hours.

Comments per review from humans should decrease in volume but increase in substance. Fewer "fix the spacing" comments, more "this approach will not scale because..." comments.

Review cycles should decrease. Fewer round-trips for mechanical fixes means PRs merge faster.

Defect escape rate should decrease for mechanical issues (style, types, accessibility) and stay the same for architectural issues. If your defect escape rate for architectural issues also decreases, it means your human reviewers are spending their time better.

The Cultural Shift

The hardest part of adopting AI code review is cultural, not technical. Some developers feel threatened. Some feel their review skills are being devalued. Some distrust AI feedback.

Address this directly: AI handles the mechanical work so humans can do the intellectual work. A reviewer who spent 30 minutes finding style issues and 10 minutes on architecture can now spend 40 minutes on architecture. That is not a demotion — it is a promotion.

The code review is not dead. It is evolving into something more valuable. The teams that adapt their process will ship faster with fewer defects. The teams that do not will either waste AI or waste their people.

AI
Architecture
TypeScript
Testing
Performance
Code Review
Refactoring
Accessibility

Related Articles

Contact

Let’s Connect

Have a question or an idea? I’d love to hear from you.

Send a Message