What Broke When I Let AI Handle My Code Reviews (And How I Fixed It)

Source: Dev.to

I – The problem you don’t know you have

Here’s what most developers are doing right now:

• They write code.
• They paste it into ChatGPT or Claude and ask, “Is this good?”
• The AI says yes (or suggests minor changes).
• They merge it.

They think they’re being efficient, but they’re actually building technical debt at scale.

The issue isn’t the AI’s output. It’s what happens to your brain when you outsource judgment.

When you let AI handle code review, you stop asking why. You stop questioning architecture. You stop seeing patterns across your codebase because you’re no longer forced to hold multiple contexts in your head simultaneously.

Code review isn’t just about catching bugs; it’s about maintaining a mental model of how your entire system works.

The printing press rendered scribes obsolete. Before Gutenberg, bookmakers employed dozens of trained artisans to hand‑copy manuscripts—a skill that took years to master. Before they knew it, that skill set was worthless.

But the editor emerged—a role whose job was deciding what was worth printing in the first place.

The pattern is that skills abstract upward.

You don’t need to be the person who writes every line, but you absolutely need to be the person who knows which lines matter.

So why is this time different?

Because most developers are using AI like a spell‑checker when they should be using it like a research team.

II – Single‑model review is a guess dressed up as certainty

I was using Claude Opus 4.1 for everything. Great model, excellent at analysis, strong with TypeScript—but it has blind spots.

One day I asked it to review a React component that was re‑rendering unnecessarily. Claude suggested memoization. Reasonable. I shipped it.

Then I ran the same code through GPT‑5 out of curiosity. GPT pointed out that the real issue was prop drilling—the memoization was treating a symptom, not the cause.

That’s when it clicked.

Every AI model is trained differently. Every model has different strengths:

• Claude excels at nuanced analysis.
• GPT is better at architectural patterns.
• Gemini catches edge cases Claude misses.

When you only use one model, you’re not getting a code review—you’re getting that model’s perspective, and that perspective has gaps you can’t see because you’ve stopped looking.

The gap between mediocre and great is taste. When anyone can generate code, the ability to know which code to trust becomes the skill.

This is where running the same review across multiple models stops being a feature and starts being a different way of thinking.

III – What actually broke (and what it taught me)

The production bug was subtle: a caching layer that worked fine in staging but failed under load. The AI had reviewed the logic—the logic was correct. What the AI didn’t catch was the assumption baked into the implementation—that cache invalidation would happen synchronously.

Why didn’t the AI catch it?

Because I didn’t give it enough context. I pasted the function, but I didn’t show the entire data flow or explain the deployment architecture. The AI can only review what you show it, and when you’re moving fast, you show it the minimum.

Three predictable failure modes

People who figure this out don’t abandon AI; they stop treating it like an oracle and start treating it like a team of junior developers who need direction.

IV – How to actually use AI for code review (without destroying your judgment)

Most developers never leave Level 1. Here’s how to move up:

Step 1: Stop reviewing in isolation

Don’t paste isolated functions. Paste the entire context: the caller, the data flow, the deployment constraints. When you extract patterns from documentation before you review, you catch assumptions the AI can’t see.

Step 2: Use multiple models, always

Run your code through at least three different models. Look for disagreement—disagreement is a signal that something is worth investigating. The bottleneck isn’t getting reviews; it’s knowing which perspective to trust before you ship.

Step 3: Make the AI show its work

Don’t accept a single “yes” or “no.” Ask the model to explain why it recommends a change, to walk through the reasoning, and to point out any assumptions it’s making. This forces the AI to expose its thought process and gives you a chance to validate—or reject—it.

V – The protocol

Here’s exactly what I do now:

Morning: Architecture Review (20 minutes)

Before writing any code, I ask three models the same architectural question:

“Given [system constraints], what are the trade‑offs of implementing [feature] this way?”

I look for disagreement. Where they agree, I move fast. Where they disagree, I slow down and think.

During Development: Context‑Rich Prompts

I don’t review individual functions anymore. I review:

• The function
• The caller
• The data flow
• The error handling
• The deployment context

I paste all of it every time.

Before Merging: Multi‑Model Analysis

I run the final code through:

• Claude Sonnet 4.5 – logical analysis
• GPT‑5 – architectural patterns
• Gemini 2.5 Pro – edge cases

Where they converge, I trust. Where they diverge, I investigate.

Weekly: Review What Broke

Every Friday I look at the week’s bugs, go back to the AI reviews, identify what the AI missed and why, and update my prompts.

Goal: not perfection, but iteration—building a system that gets smarter every week.

VI – What this actually costs you

You’re thinking: “This sounds like more work, not less.”

You’re right. Using AI well takes effort—more effort than using it poorly, and more effort than not using it at all. But here’s what you get:

The extra 10 minutes isn’t overhead; it’s an investment. You’re not just reviewing code—you’re training yourself to see what matters.

• Fast‑but‑short‑sighted developers will be quick for six months, then spend six months debugging the mess they created.
• Those who slow down now will be faster in six months because their judgment will be far sharper.

VII – The questions you should be asking

• Are you using AI to think faster or to avoid thinking?
• When AI suggests a change, can you explain why that change is better?
• If you couldn’t use AI tomorrow, would your code quality drop?

If you answered honestly, you’re probably uncomfortable. That discomfort is the point—it means you’ve been outsourcing judgment and calling it efficiency.

The gap is opening right now:

• Developers who use AI as a crutch vs. developers who use it as leverage.
• Those who let models think for them vs. those who orchestrate multiple perspectives into better decisions.

The shift

Code review has been abstracted upward. You don’t need to catch every semicolon, but you must understand the system.

• AI won’t replace you.
• Developers who know how to orchestrate AI will replace those who don’t.

The ones who figure this out in the next six months will be building at a completely different level. The ones who keep pasting code into ChatGPT and accepting whatever comes back will keep wondering why their production environment is on fire.

Intelligence should be fluid, not fragmented.
You don’t pick a side—you orchestrate all of them.

— Leena :)