How to Code Review AI-Generated Code: A Practical Guide for Engineering Teams

ByAlfred Eperjesi

AI coding assistants — Copilot, Cursor, Claude Code, and their peers — have changed what it means to write software. A developer who once wrote 200 lines a day can now produce 2,000. That sounds like a win. In most teams right now, it is not.

The problem is not the tools. The problem is that code review practices have not kept pace. Teams are reviewing AI-generated code the same way they reviewed human-written code five years ago — line by line, checking for obvious bugs, merging when it looks roughly right. That approach was barely adequate then. Applied to AI output at 10x the volume, it is a debt accumulation machine.

This guide covers what changes when you are reviewing AI-generated code, and what your team needs to do differently.

Why AI-Generated Code Fails Differently

Human developers make mistakes rooted in misunderstanding, fatigue, or shortcuts under pressure. AI assistants make mistakes rooted in pattern matching without context. The distinction matters because the failure modes look different:

  • Plausible but wrong — AI code often looks correct at a glance. It compiles, it follows conventions, it is well-formatted. The error is logical or architectural, not syntactic.
  • Context blindness — the model does not know your domain, your existing abstractions, or your team’s decisions. It will reinvent wheels, ignore established patterns, and duplicate logic already in the codebase.
  • Confident incorrectness — unlike a junior developer who might flag uncertainty, AI output arrives with no caveats. There is no signal to tell the reviewer “I wasn’t sure about this part.”
  • Security blind spots — AI models are trained on public code, which includes a great deal of insecure code. SQL injection, improper input validation, and insecure defaults appear regularly in AI-generated output.

What to Look For: A Review Checklist

When reviewing AI-generated code, structure your review around these areas:

1. Architectural fit

Does this code belong where it has been placed? Does it follow the existing layering, dependency rules, and module boundaries of the codebase? AI assistants frequently generate code that works in isolation but violates the architectural decisions the team has made. These violations are cheap to catch at review time and expensive to unpick later.

2. Duplication

AI assistants do not have a global view of your codebase. They generate solutions for the problem in the prompt, unaware that the same solution already exists elsewhere. Before accepting AI output, search for existing implementations of the same behaviour. Duplicated logic is one of the fastest routes to inconsistency and bugs.

3. Error handling

AI-generated code frequently has shallow or missing error handling. It handles the happy path well. Check every external call, every I/O operation, and every state transition: what happens when it fails? Is the failure surfaced correctly? Is it logged? Is the system left in a consistent state?

4. Security

Treat every piece of AI-generated code that touches user input, external data, authentication, or persistence as untrusted until proven otherwise. Check for: unsanitised inputs, hardcoded credentials, overly permissive configurations, and use of deprecated or insecure library methods.

5. Test coverage

AI assistants can generate tests as well as implementation code — but the tests they generate tend to test the implementation rather than the behaviour. Check that tests cover meaningful scenarios, including edge cases and failure paths, not just the happy path the AI was prompted to build.

6. Dependency choices

AI assistants frequently introduce new library dependencies to solve problems your existing dependencies already handle, or suggest libraries that are unmaintained or have known vulnerabilities. Review every new import and question whether it is necessary.

Change How You Think About Review Volume

A pull request containing 2,000 lines of AI-generated code is not equivalent to a PR containing 2,000 lines of carefully written human code. It requires more scrutiny, not less — because the author cannot explain their reasoning, flag uncertainty, or answer questions in standup.

Some practical approaches:

  • Set a PR size limit regardless of origin. If your team previously kept PRs under 400 lines, keep that limit. AI makes it easy to generate large changesets; it does not make large changesets easier to review.
  • Require the developer to explain the approach before the code is reviewed. If they cannot explain what the AI generated in plain terms, the review should not proceed.
  • Run automated checks first. Static analysis, security scanning, and test coverage gates should run before a human reviewer looks at the code. Do not waste reviewer attention on things tools can catch.

Build Guardrails, Not Just Guidelines

Guidelines that live in a wiki get ignored under deadline pressure. Guardrails that run in CI enforce standards consistently regardless of pressure. For AI-generated code at scale, you need both:

  • Linting and formatting enforced in CI (not just recommended in the style guide)
  • Architecture fitness functions that fail the build when structural rules are violated
  • Dependency vulnerability scanning on every PR
  • Test coverage thresholds that cannot be merged below

The goal is to reduce the cognitive load on human reviewers so they can focus on the things that tools cannot catch: whether the code is solving the right problem, in the right place, in a way that is consistent with where the system is going.

The Bottom Line

AI coding tools are not going away, and the teams that use them well will outship the teams that do not. But “using them well” means building the engineering discipline and review practices to make the output trustworthy — not just fast.

The teams struggling with AI adoption right now are not struggling because the tools are bad. They are struggling because they adopted the tools without updating the practices around them. Code review is the most important practice to get right.

At Cloudomation, we help engineering teams build the quality practices and architectural guardrails to use AI coding tools effectively. If your team is shipping more code but accumulating more debt, get in touch for a Quality Engineering Assessment.

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