AI Code Review: Using AI to Check Your Own Code

Why I Started Using AI for Code Reviews

I'll be honest — I used to be that developer who'd submit a PR and then immediately spot three obvious issues after clicking "Create Pull Request." You know the feeling, right? That sinking moment when you realize you could have caught that bug yourself if you'd just looked a little closer.

That changed when I discovered I could use AI tools as my personal code reviewer. Now, before any code leaves my machine, it gets a thorough AI review. It's like having a senior developer sitting next to me, but one who never gets tired of explaining why that nested loop might be inefficient.

The best part? This isn't about replacing human code reviews — it's about making them more valuable. When your teammates review AI-checked code, they can focus on architecture and business logic instead of catching basic syntax errors.

Setting Up Your AI Code Review Workflow

The key to effective AI code review is making it easy enough that you'll actually do it. I've tried complex workflows that required 10 steps, and guess what? I stopped using them after a week.

Here's my simple approach: I use three different AI tools depending on the situation, and I've created templates for each. Let me show you how I set this up.

Method 1: Quick Reviews with ChatGPT

For small functions or quick fixes, I use ChatGPT with a simple prompt template. This is perfect when I just want a sanity check on a piece of code.

I keep this template saved in a text file because typing it out every time gets old fast. The beauty of this approach is that ChatGPT gives you feedback in plain English, not cryptic error messages.

When I used this on a React component last week, it caught that I was calling setState inside a loop — something that would have caused performance issues but wasn't throwing any errors.

Method 2: Deep Dives with Claude

For more complex code or when I want a really thorough review, I use Claude. It's particularly good at understanding context and providing detailed explanations.

Here's my Claude review process:

The "rank by severity" part is crucial. Claude will tell you what needs immediate attention versus what's just a nice-to-have improvement. This has saved me from spending an hour optimizing something that wasn't actually a problem.

Method 3: Real-time Reviews with Cursor

When I'm actively coding, I use Cursor for real-time code review. Instead of waiting until I'm done, I can get feedback as I write.

My favorite Cursor trick is the "explain and improve" command. I highlight a function and ask:

Cursor understands your entire codebase context, so it can spot issues like "this function does the same thing as the utility you wrote in utils.js" — something that's impossible with isolated code snippets in ChatGPT.

What to Look for in AI Code Reviews

After doing hundreds of AI code reviews, I've learned to focus on specific types of feedback. Not all AI suggestions are created equal.

Always take seriously:

• Security vulnerabilities (SQL injection, XSS, etc.)
• Logic errors that could cause crashes
• Performance issues with loops or database queries
• Memory leaks or resource management problems

Consider carefully:

• Style suggestions (depends on your team's standards)
• Refactoring recommendations (weigh the benefits vs. time cost)
• Alternative approaches (great for learning, but not always necessary)

Often ignore:

• Overly verbose variable name suggestions
• Micro-optimizations that don't affect real performance
• Suggestions that conflict with your existing codebase patterns

The key is developing judgment about what matters. When I first started, I tried to implement every suggestion. Now I focus on the ones that actually improve the code's reliability and maintainability.

My Pre-PR Checklist

Here's the exact process I follow before submitting any pull request:

1. Write the code (obviously)
2. Self-review once — just reading through manually
3. AI review — using one of the methods above
4. Fix critical issues — anything that could break or cause security problems
5. Consider improvements — implement the suggestions that make sense
6. Test the changes — make sure my fixes didn't break anything
7. Submit the PR with confidence

This process takes maybe 10 extra minutes, but it's saved me hours of back-and-forth with reviewers. Plus, my teammates have started commenting on how much cleaner my code has gotten.

Real Example: Catching a Subtle Bug

Let me share a real example from last month. I had written a function to process user uploads:

Looked fine to me. But when I ran it through Claude, it immediately spotted the issue: processFile() was an async function, but I wasn't awaiting it. My function was returning an array of promises, not processed files.

Claude suggested using Promise.all() and explained exactly why my approach wouldn't work. That's the kind of bug that would have made it through manual review but caused real problems in production.

Making It Stick

The hardest part isn't learning how to use AI for code review — it's building the habit. Here's what worked for me:

Start small: Begin with just one tool and simple prompts. Don't try to set up the perfect workflow on day one.

Make it fast: If your process takes more than 5 minutes, you won't do it consistently. Save templates, use keyboard shortcuts, whatever it takes.

Track the wins: I keep a simple note of bugs AI review has caught. Seeing that list grow is great motivation to keep doing it.

Don't be precious: Not every piece of code needs a deep AI review. A quick sanity check on small changes is often enough.

The goal isn't perfection — it's shipping better code more consistently. AI code review has made me a more confident developer because I know my code has been looked at by something that never gets tired and never misses obvious mistakes.

Give it a try on your next feature branch. I bet you'll be surprised by what you learn about your own code.