AI can generate a working implementation in seconds.

That doesn't mean the implementation is correct.

For developers, this creates a slightly different code-review problem. You're no longer reviewing only code written from scratch by someone who understands the surrounding system. You're often reviewing code generated from a prompt, existing examples, partial context, or a combination of all three.

The result can compile, pass tests, and look perfectly reasonable while still being wrong.

The answer isn't to distrust every line of AI-generated code. It's to review it differently.

This article presents a practical approach for reviewing AI-generated changes without spending an hour reading every line with equal attention.

1. Why AI-Generated Code Is Different

AI-generated code has one particularly important characteristic:

It can be locally convincing while being globally incorrect.

Consider a simple API endpoint:

@app.get("/users/{user_id}")
def get_user(user_id: int, db: Session = Depends(get_db)):
    user = db.query(User).filter(User.id == user_id).first()

    if not user:
        raise HTTPException(status_code=404, detail="User not found")

    return user

At first glance, this looks fine.

But several questions immediately matter:

• Is the endpoint authenticated?
• Can any authenticated user retrieve another user's data?
• Is User safely serializable?
• Does returning the ORM object expose fields that shouldn't leave the API?
• Is there an authorization requirement that isn't represented in this function?

The code itself doesn't contain enough information to answer those questions.

That's common with AI-generated code.

The model optimizes for producing a plausible implementation from the available context. It doesn't automatically understand your organization's security model, undocumented business rules, production traffic patterns, or architectural constraints.

So code review needs to evaluate two things:

What does this code do?

and

What assumptions does this code make?

The second question is often where the important bugs are.

2. What Should Reviewers Check First?

Don't start by reading every line.

Start with the change itself.

Before looking at implementation details, answer:

What problem is this PR solving?

If the PR description says:

"Add caching to improve API performance."

you should immediately ask:

• What endpoint?
• What data is cached?
• How long?
• What invalidates it?
• Is stale data acceptable?
• Is the cache shared between users?

Then look at the diff.

A useful first pass is:

1. Understand the intended behavior.
2. Identify the files changed.
3. Look for changes to security boundaries.
4. Look for database and external-service interactions.
5. Look for new state or caching.
6. Check error-handling paths.
7. Only then inspect implementation details.

This prevents a common review failure: spending ten minutes debating naming while missing a broken authorization check.

3. Logic and Edge Cases

AI-generated code frequently handles the happy path well.

The interesting bugs tend to live elsewhere.

Suppose an AI-generated function processes payments:

function calculateRefund(order, requestedAmount) {
    if (requestedAmount <= order.total) {
        return requestedAmount;
    }

    return order.total;
}

Looks harmless.

But what happens with:

calculateRefund(order, -100);

The function returns -100.

What about floating-point currency?

0.1 + 0.2 !== 0.3

What about an already-refunded order?

What about concurrent refund requests?

What about an order whose total has changed after partial refunds?

The important question isn't:

"Does this function work?"

It's:

"Under what conditions does this function stop working?"

For AI-generated code, explicitly test:

• Empty input
• Null/undefined values
• Zero values
• Negative values
• Very large values
• Duplicate requests
• Concurrent requests
• Missing records
• Deleted records
• Partial failures
• Timeouts
• Retries
• Invalid state transitions

Consider this common pattern:

if not user:
    create_user(email)

That looks reasonable until two requests arrive simultaneously.

Both requests can observe that the user doesn't exist.

Both attempt to create it.

Now you've discovered a race condition.

The database should usually enforce the invariant as well:

CREATE UNIQUE INDEX users_email_unique
ON users(email);

Then the application needs to handle the constraint violation appropriately.

Don't assume the generated code has considered concurrency simply because the sequential logic looks correct.

4. Security

Security deserves an earlier pass than ordinary correctness.

AI-generated code can accidentally introduce vulnerabilities because security depends heavily on context.

SQL injection

Bad:

query = f"SELECT * FROM users WHERE email = '{email}'"
db.execute(query)

Better:

query = text("SELECT * FROM users WHERE email = :email")
db.execute(query, {"email": email})

But parameterized queries aren't the end of the review.

Ask:

• Is authorization enforced?
• Is sensitive information logged?
• Are secrets exposed?
• Is user-controlled input trusted?
• Are uploaded files validated?
• Are redirects controlled?
• Are permissions checked server-side?
• Are internal errors exposed to clients?

For example:

app.get("/admin/users", authenticate, async (req, res) => {
    const users = await getUsers();
    res.json(users);
});

Authentication exists.

Authorization might not.

A reviewer should ask whether req.user actually has permission to access the endpoint.

Logging is another common problem

An AI-generated debugging statement might look innocent:

logger.info("Payment request: %s", request.json)

If the request contains card information, authentication tokens, addresses, or other sensitive fields, you've just created a data-exposure problem.

Review security based on data flow, not just individual functions.

5. Tests

Passing tests don't prove that generated code is correct.

They prove that the tested scenarios currently behave as expected.

AI-generated tests can have another problem: they may simply encode the implementation rather than validate the requirement.

For example:

def test_discount():
    result = calculate_discount(100, 10)
    assert result == 90

Useful, but incomplete.

What about:

calculate_discount(100, 0)
calculate_discount(100, 100)
calculate_discount(100, -10)
calculate_discount(100, 110)
calculate_discount(0, 10)

More importantly, ask whether the tests represent actual business requirements.

A strong review checks:

Coverage of behavior

Not just line coverage.

You want coverage of:

• Happy paths
• Failure paths
• Boundary conditions
• Authorization
• State transitions
• Retries
• Timeouts
• Concurrency where relevant

Test independence

Watch for tests that depend on execution order or shared mutable state.

Mock realism

AI-generated tests often mock everything:

mock_db.return_value = fake_user
mock_api.return_value = {"status": "ok"}

The test may pass while the real integration is broken.

Mocks should isolate behavior intentionally, not hide the behavior you're supposed to verify.

6. Architecture

This is where human review becomes particularly important.

AI is very good at producing an implementation that fits the immediate request.

It isn't necessarily good at knowing whether that implementation belongs in your architecture.

Imagine a service that adds:

const result = await database.query(...);
const response = await externalApi.call(...);
await cache.set(...);
await sendEmail(...);

All inside one HTTP controller.

Each operation may work.

The architectural problem is that one request handler now owns:

• persistence
• external integration
• caching
• notifications
• business logic

The PR might technically work while making the system harder to evolve.

Ask:

• Does this belong in this layer?
• Are responsibilities separated?
• Is existing architecture being bypassed?
• Does this introduce a new abstraction unnecessarily?
• Does this duplicate existing functionality?
• Does this create a new dependency?
• Does the data flow still match the system's boundaries?

AI-generated code often creates new helpers or abstractions when an existing one already exists.

Search the repository before approving new infrastructure.

The best implementation may already be somewhere in the codebase.

7. Maintainability

Readable code isn't necessarily maintainable code.

Look for unnecessary complexity.

For example:

const shouldProcess =
    user &&
    user.status &&
    user.status !== "disabled" &&
    (!user.deletedAt || user.deletedAt === null) &&
    permissions &&
    permissions.includes("write");

The code works, but the condition is difficult to reason about.

Could the domain logic be clearer?

function canWrite(user, permissions) {
    if (!user || user.status === "disabled") {
        return false;
    }

    if (user.deletedAt) {
        return false;
    }

    return permissions?.includes("write") ?? false;
}

During review, consider the next developer.

Will they understand why this code exists?

Can they modify it safely?

Does it introduce duplicated logic?

Does the naming describe the domain?

Is there enough context around non-obvious decisions?

AI can produce syntactically clean code very quickly. That doesn't automatically make the codebase easier to maintain.

8. Common AI-Review Mistakes

There are several ways reviewers can make AI-generated PRs harder to review.

Mistake 1: Reviewing every line equally

Not every line has the same risk.

Focus attention on:

• Security boundaries
• Database mutations
• Authentication/authorization
• Concurrency
• External APIs
• State changes
• Error handling

Mistake 2: Trusting green tests

Tests are evidence.

They're not proof.

Ask what isn't tested.

Mistake 3: Focusing on style before behavior

Don't spend ten comments discussing variable naming while missing incorrect business logic.

Automate formatting and linting wherever possible.

Mistake 4: Assuming generated code understands your system

It doesn't have your entire organizational context.

Review assumptions explicitly.

Mistake 5: Ignoring PR size

A 1,500-line AI-generated PR is difficult to review regardless of how clean the code looks.

Breaking changes into smaller PRs makes correctness easier to establish.

Mistake 6: Asking AI to review AI without verification

An AI reviewer can be useful for finding patterns humans might miss.

But its findings still require validation.

Don't replace human judgment with another generated judgment.

9. A Practical AI-Code Review Checklist

Before approving an AI-assisted PR, run through this checklist.

Context

• [ ] Do I understand what problem this PR solves?
• [ ] Is the scope clear?
• [ ] Is the PR reasonably sized?

Logic

• [ ] Does the implementation match the requirement?
• [ ] What assumptions does the code make?
• [ ] What happens with empty, invalid, or extreme input?
• [ ] Are race conditions possible?
• [ ] Are retries and duplicate requests safe?

Security

• [ ] Is authentication correct?
• [ ] Is authorization enforced?
• [ ] Is user input handled safely?
• [ ] Are secrets protected?
• [ ] Could sensitive information reach logs?
• [ ] Are errors exposing internal information?

Data

• [ ] Are database queries safe?
• [ ] Are transactions required?
• [ ] Are constraints enforced at the database level?
• [ ] Could concurrent requests corrupt state?
• [ ] Is caching introducing stale or cross-user data?

Tests

• [ ] Are important behaviors tested?
• [ ] Are edge cases covered?
• [ ] Are failure paths tested?
• [ ] Are tests testing requirements rather than implementation details?
• [ ] Are mocks hiding important integration behavior?

Architecture

• [ ] Does the change fit existing patterns?
• [ ] Is there already code that solves this problem?
• [ ] Are responsibilities in the right layer?
• [ ] Does the change introduce unnecessary dependencies?

Maintainability

• [ ] Is the code understandable?
• [ ] Is complexity justified?
• [ ] Are names meaningful?
• [ ] Is duplication introduced?
• [ ] Will another developer understand the reasoning six months from now?

The Goal Isn't to Review More. It's to Review Better.

AI changes the economics of writing code.

When producing another function, test, refactor, or API endpoint becomes dramatically cheaper, teams can generate changes faster than humans can carefully inspect them.

That creates a new bottleneck:

engineering attention.

The answer isn't to manually inspect every generated line forever.

It's to make review more deliberate.

Start with the highest-risk behavior.

Question assumptions.

Test boundaries.

Trace data.

Inspect security boundaries.

Understand architectural consequences.

And keep changes small enough that another engineer can actually understand them.

AI can make code generation cheap.

Human attention is still expensive.

Code review should spend that attention where it has the highest chance of preventing something from reaching production.