November 9, 2026 · 3 min read

How to Review AI-Written Code Before It Ships

AI coding tools write code fast. Here's the review routine that keeps that speed from turning into bugs and security holes.

Review AI-written code the way you'd review code from a fast, confident new hire: assume it runs, and check whether it's right, safe, and something your team can maintain. The habits below take minutes per change and catch the problems AI tools tend to introduce.

Why AI-written code needs its own checks

AI coding assistants are good at producing code that looks finished. They're less good at knowing your business rules, your security requirements, and the quirks of your existing system. The result is code that passes a quick glance and fails in production, or works today and becomes impossible to change next month.

The fix isn't to stop using the tools. It's to make review a habit, with a short list of things AI tends to get wrong.

Start with what the change is supposed to do

Before reading a line, write one sentence describing what the change should do and what it must not break. If the person who prompted the AI can't write that sentence, the change isn't ready for review.

The review checklist

Secrets: no API keys, passwords, or tokens in the code or in logs.
Sign-in and permissions: every new page or endpoint checks who's asking and what they're allowed to see.
Input: anything from a user or another system is validated before it's used, especially in database queries.
Dependencies: new packages are real, maintained, and actually needed. AI tools sometimes suggest packages that don't exist.
Errors: failures are handled and reported, not silently swallowed.
Duplication: the change reuses what already exists instead of quietly adding a second version.
Tests: there's a test for the behavior that matters, and it fails when that behavior breaks.

Ask the AI to explain, then check the explanation

A useful habit is to ask the tool to explain the change in plain language and list its assumptions. Then compare that list with what you know about the system. Wrong assumptions are the fastest way to find wrong code.

Make good habits shared, not personal

On most teams, one or two engineers figure out how to work well with AI and everyone else improvises. Write down what works: the prompts, the instructions you give the tool about your codebase, and the review checklist. Keep them in the repository, where the whole team and the AI tools can use them.

Where we help

Our AI for Software Engineering training covers AI coding tools and agents, reviewing and testing what they produce, and turning your best engineers' habits into team conventions. If an app built with AI is already running part of your business, our Vibe Code Cleanup service reviews and hardens it.

Our AI for Software Engineering training

Questions people ask

01Is AI-written code less secure?

Not by definition, but it's often written without knowledge of your security requirements, and it can look more finished than it is. Treat it like any code from someone new to the system: review sign-in, permissions, input handling, secrets, and dependencies.

02Should we ban AI coding tools?

Usually not. Banning them tends to push their use out of sight. It's safer to approve tools, agree on how they're used, and make review a habit.

03Do we need new tools to review AI code?

No. Your existing pull request process works if reviewers know what to look for. Automated tests and security scanning help, but they don't replace a person who understands what the change should do.