How to Review AI-Generated Code Before You Commit It

TL;DR

AI-generated code has predictable failure modes: deprecated APIs, hallucinated functions, edge case blindspots, and security gaps.
"It runs" is not the same as "it's correct." Reading AI output before committing is not optional.
Check that all referenced functions and libraries actually exist. AI tools hallucinate these regularly.
Test edge cases the AI didn't think to cover. They're usually there.
The discipline of reviewing AI code is actually a good way to learn. You see patterns you wouldn't generate yourself.
Paste-and-hope is how bugs get into production and how engineers lose credibility fast.

Most developers who use AI coding tools have had the experience of getting code that looks right, pasting it in, and watching something break in a way they didn't expect. The code ran fine. It just didn't handle the edge case they cared about. Or it called a function that doesn't exist. Or it used a pattern that works in a tutorial context but not in production.

This is not an argument against using AI tools. It's an argument for treating AI-generated code the way you treat code from any source you don't fully trust yet: read it, test it, understand it before you commit it.

This is also relevant to writing better prompts to get better output in the first place. Better input reduces but doesn't eliminate the need for review.

The Specific Ways AI-Generated Code Fails

Understanding why AI code fails makes it easier to catch the failures. There are a few patterns that show up consistently.

Deprecated or Removed APIs

AI models have training cutoffs. They don't know that a function was deprecated in the version you're running, or that a library changed its API significantly in the last major release. The generated code may have worked perfectly two years ago. It may not work at all now.

This is especially common with fast-moving ecosystems: JavaScript frameworks, Python ML libraries, cloud provider SDKs. Any time you're working with something that changes frequently, verify that the specific functions and method signatures the AI generated actually exist in the version you have installed.

Hallucinated Functions and Methods

AI tools sometimes generate calls to functions that don't exist anywhere. They sound plausible. They follow the naming conventions of the library. They just aren't real.

This is distinct from deprecated code. Deprecated code existed once. Hallucinated functions never existed. If you don't read the code and just run it, you'll get a NoMethodError or AttributeError and spend time debugging something that was never going to work.

Checking this is simple: look up every external function call the AI generated. Confirm it exists in the docs or in the source. This takes minutes and saves real time later.

Code That Passes Basic Tests But Fails Edge Cases

AI-generated code often handles the happy path well. It handles the input format that was implied by your prompt. It doesn't necessarily handle:

Empty inputs or empty arrays
Null or nil values where a value was expected
Inputs at the boundary of valid ranges
Concurrent requests or race conditions
Network failures or timeouts in code that makes external calls
Malformed data from external sources

These aren't AI-specific failures. They're the same edge cases that trip up junior engineers writing code from scratch. But AI code tends to look more polished, which makes it easier to overlook that the edge cases weren't considered.

Security Vulnerabilities

AI tools don't have a security focus built in by default. They generate code that does the thing you asked. They may not generate code that does it safely.

Common issues: SQL injection via string interpolation when you should be using parameterized queries, missing input validation on user-supplied data, inadequate authentication checks, secrets or credentials embedded in code that should come from environment variables, missing CSRF protection, and overly permissive CORS settings.

These aren't always subtle. They're often the exact antipatterns that are covered in any basic security review. But if you're pasting AI output without reading it, you'll miss them.

Patterns That Don't Fit Your Codebase

AI code is correct in isolation but inconsistent with your existing codebase. It uses a different error handling pattern than the rest of your code. It directly queries the database instead of going through your existing service layer. It uses a third-party library you don't want to add as a dependency.

This is less about bugs and more about the long-term cost of inconsistency. Code that doesn't fit the surrounding patterns is harder to maintain, harder for teammates to read, and signals to reviewers that it wasn't thought through.

A Review Process That Works

You don't need a formal checklist for every two-line snippet. You do need a consistent habit for anything that's going into a codebase you care about.

Step 1: Read It First

Before you run it, read it. All of it. Not just the function signature. Read the implementation.

This sounds obvious. Many developers skip it. The AI gave you something, it looks about right, you want to move on. Reading it slows you down by three minutes and catches things that would cost you two hours later.

When you read it, ask: do I understand what each part of this is doing? If a section is unclear, that's a signal to either ask the AI to explain it or look it up yourself. Committing code you don't understand means you won't be able to debug it when it breaks.

Step 2: Verify External Dependencies

Make a list of every library, gem, package, or module the code imports or calls that you didn't specify. Then verify:

Does it actually exist?
Is it already in your project's dependencies? If not, is adding it appropriate?
Is the version compatible with what you're running?
Is it actively maintained, or is it abandoned?

For every external function call: check the docs. Confirm the method signature matches what the AI generated. This is especially important if the API might have changed recently.

Step 3: Run It and Look at What It Actually Does

Run the code with real input. Not just the happy path input. Feed it edge cases.

What happens with an empty array? A nil where a value was expected? A string where a number was expected? A very large input? A zero?

If the code makes external calls, what happens when those calls fail? If it writes to a database, what does the database actually look like after it runs?

Running the code in isolation, before it's integrated with everything else, is the fastest way to find the simple bugs. It also confirms you actually understand what the output is.

Step 4: Check for Security Issues

For any code that touches user input, authentication, database queries, or external services, ask specifically:

Is user input ever used directly in a query or command? It should always be parameterized or escaped.
Are there any credentials, API keys, or secrets hardcoded in the code? They should come from environment variables.
Does the code validate input before using it?
Are there any operations that should require authentication checks that are missing them?

You don't need to be a security expert to catch the common things. Most AI-generated security issues are the well-known categories, not obscure ones.

Step 5: Check Consistency with Your Codebase

Compare the generated code to similar existing code in your project.

Does it follow the same patterns for error handling? Does it use the same naming conventions? Does it interact with other parts of the system (models, services, APIs) the way your existing code does?

If it doesn't, you have a choice: adapt it to fit, or accept the inconsistency and document why. Either is fine. What's not fine is committing code that doesn't fit and not noticing.

The Discipline of Reading Before Committing

The paste-and-hope workflow (paste the AI output, run it, ship it if tests pass) is how most AI-related bugs get into production. It's also one of the ways early-career engineers burn their credibility on a team.

When you commit code in a professional environment, you're vouching for it. It doesn't matter that the AI wrote the first draft. If you committed it, you're responsible for it. A code review that uncovers a hallucinated function call or an obvious SQL injection in code you submitted is embarrassing. A production incident caused by code you didn't read is worse.

The standard for what "good enough" code actually means on a real team applies to AI-generated code the same way it applies to anything else you write. "It works on my machine" isn't the bar. "I understand it, it handles failure cases, and it's consistent with how we do things here" is closer to the bar.

What Reviewing AI Code Teaches You

There's a learning benefit to this that doesn't get talked about enough.

When you generate code from scratch, you make decisions based on what you know. You use the patterns you're already familiar with. When you read AI-generated code carefully, you sometimes see approaches you wouldn't have thought to use.

That only works if you actually read it and ask why. "The AI used a memoization decorator here. I wouldn't have thought to do that. Why would you use that pattern in this context?" is a question worth asking. If the answer makes sense, you've learned something. If it doesn't make sense for your situation, you've caught a problem.

AI code review is an underrated path to learning patterns that are outside your current comfort zone. It doesn't replace reading documentation or understanding fundamentals. But it exposes you to code patterns faster than writing everything yourself.

This is one version of using AI as a learning tool rather than just a shortcut. The review process is where the learning happens.

AI Code Review in a Team Context

If you're working on a team, AI-generated code goes through the same review process as everything else: a pull request, a code review, the standard checks.

A few things are worth knowing about this context.

Experienced reviewers can often tell when code was AI-generated and then not read. The inconsistency in style, the imports that don't fit the project's conventions, the error handling that's correct in isolation but wrong for the context. It's not always obvious, but it's more obvious than many developers think.

The way to avoid this isn't to hide that you used AI. It's to actually review and adapt the output so it fits. When AI-generated code goes through a review and comes back with comments, treat those comments the same way you'd treat any review feedback. Handling code review feedback well is a skill that applies regardless of how the code was written.

On the git side, committing AI-generated code that you haven't tested or reviewed creates problems for your team that extend beyond the immediate bug. Keeping a clean git history matters whether the commits contain AI output or not.

The Habit Worth Building

Make "read it before you run it" automatic. Every time you get AI output, before you do anything else, read through it. Note anything you don't understand. Mark anything that looks like it might not fit. Then run it, test the edge cases, and verify the dependencies.

It takes longer than paste-and-hope. It takes much less time than debugging a production issue caused by code you didn't read.

The engineers who use AI tools well aren't the ones who accept output fastest. They're the ones who can integrate AI output into real work reliably, because they've built the habit of actually understanding what they're shipping.

For the step before all of this, writing prompts that produce better initial output, read how to write prompts that get better code from AI tools. And for how professional code quality standards apply to everything you commit, what good enough code means on a real team is worth reading.