How I Replaced 80% of Code Reviews with AI

Hot take: Most code reviews are rubber stamps anyway. AI does it better.

The Problem with Human Code Review

Let's be honest:

Reviewers are busy and skim

Style nitpicks dominate real issues

Knowledge silos create blind spots

Reviews become bottlenecks

My AI Code Review Pipeline

.github/workflows/ai-review.yml

name: AI Code Review

on: [pull_request]

jobs:
ai-review:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: AI Review
uses: coderabbit-ai/ai-reviewer@v2
with:
model: claude-4-opus
rules: |
- Check for security vulnerabilities
- Identify performance issues
- Verify error handling
- Check test coverage
- Suggest improvements

What AI Catches (That Humans Miss)

1. Security Vulnerabilities

// AI flags this immediately
const query = SELECT * FROM users WHERE id = ${userId};
// 🚨 SQL Injection vulnerability detected

2. Performance Anti-Patterns

// AI spots the N+1 query
for (const user of users) {
const posts = await db.posts.findMany({ where: { userId: user.id }});
}
// 🚨 N+1 query detected. Use include/join instead.

3. Missing Edge Cases

// AI asks about null handling
function getUsername(user) {
return user.profile.name;
}
// 🚨 What if user.profile is undefined?

The Human Layer

AI handles the first 80%. Humans focus on:

Architecture decisions - Does this fit our system?

Business logic - Does this solve the right problem?

Team knowledge - Is there existing code we should reuse?

Mentorship - Teaching moments for junior devs

Results After 6 Months

| Metric | Before | After |
|--------|--------|-------|
| Review time | 4 hours | 30 min |
| Bugs in prod | 12/month | 3/month |
| Dev satisfaction | 6/10 | 9/10 |

Getting Started

1. CodeRabbit - Best for GitHub/GitLab integration
2. Sourcegraph Cody - Great for enterprise
3. Custom GPT - Roll your own with OpenAI API

The future isn't AI replacing developers. It's AI handling the tedious parts so we can focus on what matters.