We've all seen the hype. "Use AI for code reviews!" "Let Claude check your PR!" But here's the thing—I've been running AI-assisted code reviews for six months, and they're useful exactly when you stop treating them like a magic solution.
Let me share what actually works.
The Pattern Stuff: Where AI Crushes It
AI is phenomenal at finding structural issues:
- Missing error handling. You write a function that talks to an API, forget the 500 error case, and the AI calls it out immediately.
- Inconsistent naming. If your codebase uses
fetchUser()but you wrotegetUser(), an LLM catches it in seconds. - Security gaps. Using
eval()instead ofJSON.parse()? Missing SQL injection guards? It spots these. - Performance anti-patterns. N+1 queries, unnecessary loops, blocking calls in async code—these show up fast.
The key: these are structural. They don't require understanding your business logic.
How I Use It
I run this workflow:
1. Push a branch
2. GitHub Action triggers Claude on the diff
3. Get a report on structural issues (5-10 min)
4. Humans review the logic, architecture decisions, and intent
This saves maybe 15 minutes per review that a human would spend hunting for typos and obvious bugs. That time compounds.
Where It Breaks Down
AI code reviewers will miss everything about your actual product:
Logic that's wrong by design. You wrote a function to calculate prices with a 15% discount. It's syntactically perfect. The AI approves. Turns out you needed 20% for Q2, and nobody caught it because the code was "correct."
The "why" behind decisions. You're using a slower library because it's more maintainable for your team. Or you're building in a way that'll scale to what you're launching next month. An AI doesn't know this. It might suggest "faster" alternatives that are actually worse for your context.
Cross-cutting concerns. The PR looks fine in isolation. But it conflicts with a pattern your team established last quarter. AI doesn't know your conventions unless you explicitly train it on them.
The Honest Setup
Don't treat AI as a reviewer. Treat it as a first-pass filter:
- AI stage: Catch obvious bugs, style issues, security gaps, performance problems.
- Human stage: Review logic, architecture, business impact, team conventions.
Post-AI, humans are reviewing the stuff that actually matters. You've cut the noise by ~60%.
Real Example
I did this on a feature last week:
What the AI flagged:
- A database query missing pagination (could timeout on large datasets)
- Three error cases not handled
- A naming inconsistency with existing code
- A missing
.catch()on a promise chain
What I had to fix manually:
- The business logic was backwards in one function (discount applied to wrong field)
- A test case was too narrow and wouldn't catch edge cases
- The approach conflicted with our plan to move that service to a different schema next sprint
If I'd shipped after the AI approval, I'd have had problems. But without the AI pass, I'd have been squinting at those three error cases for ten minutes.
Tools That Work
If you want to try this:
- GitHub + Claude API: Use Actions to comment on PRs with structured feedback
- Local LLMs: If you're paranoid about code leaving your network, Ollama + a 13B model works surprisingly well for structural issues
- Prompting pattern: Give it your codebase style guide, then ask it to check against that specific guide. LLMs are great at following explicit rules.
Don't use:
- Pre-trained review bots that don't know your codebase (too many false positives)
- AI as your only reviewer (you'll miss business logic mistakes)
- LLMs for reviewing changes you don't understand (defeats the purpose)
The Vibe Check
The best code reviews aren't about catching every bug. They're about shared context. An AI can help you avoid the obvious stuff so humans can focus on the interesting problems—the architectural decisions, the "will this work in six months?", the stuff that actually shapes your product.
Use AI for what it's good at. Don't pretend it replaces human judgment.
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