How I Use AI to Write Better Code Reviews

Code review is one of those practices that everyone agrees is important and almost nobody thinks they’re doing well. Reviews take too long, feedback is inconsistent, and reviewers are often too busy to give the kind of thorough attention code deserves. I’ve been using AI to fundamentally change how I approach code reviews, and the results have been genuinely better than I expected.

The Problem with Traditional Code Reviews

Let’s be honest about what most code reviews actually look like. A developer opens a PR, assigns a reviewer, and then waits. Sometimes for hours, sometimes for days. When the review finally happens, the reviewer is often context-switching from their own work, which means they’re scanning rather than reading. They catch obvious issues like naming conventions and missing null checks but miss subtle logic errors because deep focus takes energy they don’t have.

The result is reviews that are either superficial (“LGTM!”) or nitpicky about style while missing actual bugs. Neither is what we want.

The AI Pre-Review: Catching Issues Before Human Eyes

My workflow now includes an AI pre-review step before any human sees the code. Here’s exactly what I do. Before opening a PR, I run through the changes with Claude Code:

> Review all changes on this branch compared to main.
  Focus on:
  1. Logic errors and edge cases
  2. Security vulnerabilities (injection, auth bypass, data exposure)
  3. Performance issues (N+1 queries, unnecessary re-renders)
  4. Missing error handling
  5. Inconsistencies with existing patterns in the codebase
  Don't flag style issues - our linter handles those.

That last line is important. AI can waste a lot of your time flagging things your linter already catches. Tell it to focus on what matters.

Prompt Patterns That Work

I’ve refined my review prompts over months of use. Here are the patterns that produce the most useful feedback:

The Security-Focused Review

> Review the changes in src/routes/payments.ts as if you're
  a security auditor. Trace every user input from the request
  to the database and identify any point where it's not
  properly validated or sanitized. Check for OWASP Top 10
  vulnerabilities.

This catches things like missing input validation on new endpoints, SQL injection through dynamic query building, and authorization checks that were accidentally omitted. In one case, it caught that a new endpoint I’d added didn’t go through the rate limiter middleware because I’d registered it before the middleware in the route stack.

The “What Could Go Wrong” Review

> Look at the changes in this branch. For each modified function,
  list the inputs or conditions that could cause it to fail or
  behave unexpectedly. Focus on edge cases that the tests don't
  cover.

This is my favorite prompt because it catches the things that break in production at 3 AM: what happens when the array is empty, when the user’s timezone is different, when the third-party API returns an unexpected response format.

The Consistency Review

> Compare the new code in this branch with the existing patterns
  in the codebase. Flag anything that deviates from established
  conventions, especially in error handling, response formatting,
  and database access patterns.

What AI Catches vs. What It Misses

After six months of this workflow, I have a clear picture of AI review strengths and weaknesses.

AI is excellent at catching:

• Missing error handling and edge cases
• Standard security vulnerabilities (injection, XSS, missing auth checks)
• Performance anti-patterns (N+1 queries, unnecessary computations in loops)
• Inconsistencies with existing code patterns
• Missing input validation
• Resource leaks (unclosed connections, missing cleanup)

AI consistently misses:

• Business logic correctness (does this feature actually do what the product team wanted?)
• Architectural appropriateness (should this even be built this way?)
• Performance issues that require understanding of production data volumes
• Subtle concurrency bugs in distributed systems
• UX implications of technical decisions

The pattern is clear: AI catches technical issues reliably. It misses contextual and judgment issues. This maps perfectly to a workflow where AI handles the first pass and humans focus on the higher-level concerns.

Integrating into Your Team’s Workflow

Here’s how I’ve rolled this out with my team without being preachy about it. I didn’t mandate anything. I just started including a section in my PRs called “AI Review Notes” that summarized what I’d caught and fixed during the AI pre-review. Within two weeks, teammates started asking how I was doing it. Within a month, three of the five team members had adopted the practice.

The key was demonstrating value, not evangelizing. When the human reviewer sees that common issues have already been caught and addressed, their review becomes faster and more focused. Everyone wins.

One more practical tip: use AI to give better review feedback too. When I’m reviewing someone else’s PR and find an issue, I’ll sometimes ask the AI to help me articulate why it’s a problem and suggest a better approach. This produces review comments that teach rather than just critique, which is better for the team’s growth.

Code review doesn’t have to be a bottleneck. With AI handling the mechanical checking, human reviewers can focus on what they’re actually good at: judgment, context, and mentorship.