Code Review for an AI-Generated MVP: What Human Engineers Should Check

Reviewing AI-generated code is not the same exercise as reviewing code written by a person on your team. The failure modes are different, and a review process built for human-written code will miss some of the most common AI-specific problems.

This is a practical checklist for engineers doing that review — what to actually look for, in what order, and why each item matters.

Why AI-Generated Code Needs a Different Kind of Review

Human developers tend to make mistakes that are visible in the code’s uncertainty — a comment asking “is this right?”, inconsistent logic, or an obviously unfinished section. AI-generated code rarely looks unfinished. It’s confident, syntactically clean, and often follows reasonable naming conventions — which makes it easy to skim past without noticing the specific things it got wrong.

That confidence is the core reviewing challenge: the code looks trustworthy whether or not it actually is.

What to Check First: Correctness of Intent

Before checking style or structure, confirm the code actually does what it was supposed to do — not just what it appears to do.

• Does the logic match the actual business rule, or a plausible-sounding approximation of it?
• Are edge cases the prompt didn’t explicitly mention (empty input, duplicate submission, expired session) handled at all?
• Does the code assume conditions that won’t always be true — like a value never being null, or a list never being empty?

AI tools generate the most likely correct implementation based on the prompt, not necessarily the actually correct one for your specific business rules. This gap is often invisible unless someone checks it against the real requirement.

Security-Specific Checks

Security issues are the most consequential category to catch in review, and also the easiest to overlook because the code often “works” in a test with a single user.

• Authentication: Is every route that should require login actually protected, or does the AI-generated route trust a client-side check alone?
• Authorization: Can a logged-in user reach another user’s data by changing an ID, parameter, or URL?
• Secrets: Are API keys, tokens, or credentials hardcoded anywhere, including in frontend-visible code?
• Input handling: Is user input sanitized and validated on the server, not just assumed safe because the frontend form restricts it?

OWASP’s guidance on common application risks specifically calls out broken access control and injection-related issues as some of the most frequent and damaging — and both are common in code generated without an explicit security-focused prompt.

Logic and Data Integrity Checks

• Are database operations that should be atomic (like deducting inventory and creating an order) actually protected against partial failure?
• Does retrying a failed action (a network timeout, a double-click) risk creating a duplicate record or charge?
• Are numeric and currency calculations handled with appropriate precision, not floating-point arithmetic that can introduce rounding errors?

Dependency and Package Checks

• Does every imported package actually exist and resolve correctly? AI tools have been known to reference plausible-sounding but nonexistent packages — a risk sometimes called “package hallucination” or slopsquatting.
• Is each dependency actively maintained, with no known critical vulnerabilities?
• Is the dependency actually necessary, or did the AI add a full library for something a few lines of code could handle?

AI coding tools and fake packages covers this specific risk in more detail — it’s a real and increasingly common issue, not a theoretical one.

Structure and Maintainability Checks

Once correctness and security are confirmed, review for longer-term cost:

• Is this logic duplicated elsewhere in the codebase, or properly centralized?
• Does this code follow the existing conventions of the project, or introduce a new, inconsistent pattern?
• Could another engineer maintain this without needing to reverse-engineer the AI’s original reasoning?

A Review Checklist Summary

Category	Key question
Intent	Does this match the actual business rule, not just a plausible guess?
Authentication	Is this route actually protected, not just assumed protected?
Authorization	Can a user reach data or actions they shouldn’t?
Secrets	Are credentials hardcoded anywhere, including the frontend?
Data integrity	Are writes protected against duplication and partial failure?
Dependencies	Are all packages real, maintained, and necessary?
Structure	Is this consistent with the rest of the codebase?

Common AI Coding Bugs Reviewers Should Watch For

A few specific bug patterns show up often enough in AI-generated code that they’re worth naming explicitly, rather than relying on a reviewer to notice them by chance.

Off-by-one and boundary errors in AI-suggested pagination or limits — code that works correctly for the first page of results but mishandles the last one, or an item count that’s consistently one higher or lower than expected.

Silent type coercion issues, particularly in dynamically typed languages, where a value that should be a number is compared against a string, producing a comparison that appears to work in casual testing but fails on specific inputs.

Copy-pasted logic with unchanged variable names, where AI generates a second, similar function by adapting an existing one, but a variable reference from the original context gets left in place, producing a subtle bug that only appears under the second function’s specific conditions.

Incomplete error propagation, where a function correctly catches an error but doesn’t pass enough information up the call stack for the caller to actually handle it appropriately, resulting in a generic failure message that hides the real cause.

None of these are unique to AI-generated code — human developers make similar mistakes — but they show up disproportionately often in AI output because the model is pattern-matching against similar code it has seen, without the same contextual awareness a human author would have about the specific codebase it’s being inserted into.

Building This Into a Repeatable Process

A one-time review is useful, but the real value comes from making this a standard step for every AI-generated feature, not just the ones that feel risky. Teams that build this habit early tend to catch issues while they’re cheap to fix — see From AI-generated code to production-ready MVP: what needs to be fixed first? for how to prioritize when a backlog of these issues has already built up.

For non-technical founders who want visibility into whether this kind of review is actually happening on their project, MVP code review checklist for non-technical founders offers a version of this process you can ask your team about directly, without needing to read code yourself.

The Goal Isn’t to Distrust AI-Generated Code

This isn’t an argument against using AI to write code — it’s an argument for reviewing it the way you’d review any code you didn’t personally verify. AI-generated code can absolutely be part of a solid, production-ready MVP. It just needs a human reviewer who knows what specifically tends to go wrong, checking for it deliberately rather than assuming a working demo means a safe product.

Frequently Asked Questions

Is reviewing AI-generated code different from reviewing code written by a junior developer?

There's overlap, but AI-generated code has its own failure pattern: it tends to be syntactically correct and confident-looking even when the logic is wrong, and it can reference packages or APIs that don't actually exist. A reviewer needs to verify claims, not just style.

How long should a code review take for an AI-generated feature?

It depends on the feature's risk level. A simple UI component might need a quick pass; anything touching authentication, payments, or customer data warrants a more thorough review regardless of how quickly it was generated.

Can automated tools replace a human review of AI-generated code?

Automated linters and static analysis are useful and should be part of the process, but they don't reliably catch logic errors, missing business rules, or security gaps that require understanding what the code is supposed to do, not just whether it runs.

What's the most commonly missed issue in AI-generated code reviews?

Access control — checking that a user can only see and modify their own data. AI tools frequently implement the feature correctly for the intended user but skip the check that stops other users from reaching the same endpoint.