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
- CodeRabbit - Best for GitHub/GitLab integration
- Sourcegraph Cody - Great for enterprise
- 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.