A Practical Review Checklist for AI-Generated Code Changes

AI-assisted coding is great at getting you from blank file to “something that compiles” quickly. The problem is that “something that compiles” is not the same as “something you can operate, secure, and maintain.”

Reviewing AI-generated changes is not about distrusting the tool. It is about acknowledging a predictable pattern: the code often looks plausible, includes reasonable names, and follows common conventions, while still hiding subtle correctness gaps, missing edge cases, or risky defaults.

This post gives you a practical checklist you can use in pull requests. The goal is to keep velocity while raising confidence, especially when code was drafted by an assistant and then edited by a human under time pressure.

Why the review needs to change (a little)

Traditional code review assumes the author understands their own change. With AI assistance, the “author” can become a curator of code they did not fully design. That shifts the reviewer’s job from “spot style issues” to “verify behavior and assumptions.”

AI-generated code tends to fail in a few repeatable ways:

• Overconfidence in edge cases: It handles the happy path well, then falls apart on nulls, time zones, retries, partial failures, and concurrency.
• Security blind spots: It forgets authorization checks, uses unsafe parsing, or logs sensitive data while being very “helpful.”
• Hidden coupling: It introduces new libraries, patterns, or abstractions that are inconsistent with your existing system.
• Test illusions: Tests exist, but they only validate the implementation, not the intent.

The fix is not a longer review. It is a more structured one.

Start with risk tiering

Before you open the diff, decide what level of evidence the change needs. A small refactor is different from code that touches auth, money movement, or data deletion. “AI-generated” is a signal to be deliberate, but the system risk still matters more than the tool used.

Suggested tiers

• Tier 1 (Low): Copy changes, comments, formatting, internal tooling, non-production scripts, or safe UI tweaks with no data access.
• Tier 2 (Medium): Business logic changes, background jobs, API integrations, caching, performance work, migrations that add fields.
• Tier 3 (High): Authentication/authorization, payments, privacy-sensitive data, destructive operations, security boundaries, multi-tenant isolation.

As the tier increases, require more from the PR: clearer intent, stronger tests, and explicit threat modeling. This keeps review consistent across your team and avoids arguing each time about what “enough” looks like.

The review checklist (copy and reuse)

Use this as a baseline. You can paste it into your team wiki or into your PR template. The idea is to remove ambiguity: reviewers check the same categories every time, and authors learn what evidence to provide.

1) Intent and scope

• Does the PR description clearly state the user-facing or operational outcome?
• Is the change set minimal, or did it bring unrelated refactors, dependency upgrades, or new patterns?
• If the assistant proposed multiple approaches, is it clear why this one was chosen?

2) Correctness and edge cases

• Are inputs validated (nulls, empty strings, bounds, unexpected enum values)?
• Are time-related behaviors explicit (time zones, locale formatting, “start of day” assumptions)?
• What happens on partial failure (network timeout, API 500, queue delay, retry)?
• Are errors surfaced in a way operators can act on (error messages, status codes, metrics)?

3) Security and data handling

• Is authorization enforced at the right layer (controller, service, query) and not only in the UI?
• Are secrets avoided in logs, exceptions, and analytics payloads?
• Are database queries scoped correctly for multi-tenant data (account_id, org_id), including joins?
• Is user input sanitized and encoded at boundaries (HTML, SQL, file paths) using your standard library methods?

4) Consistency and maintainability

• Does the code match existing conventions (naming, architecture layers, error types)?
• Did it introduce a new dependency or pattern when an existing one would do?
• Are abstractions justified, or do they add indirection without reuse?
• Would a new engineer understand the behavior from the code and comments alone?

5) Performance and operational impact

• Any new N+1 queries, unbounded loops, or heavy operations inside request handlers?
• Any new background jobs: are they idempotent, retry-safe, and observable?
• Are default limits and pagination enforced on endpoints and exports?
• Are timeouts and backpressure considered for external calls?

6) Test evidence (what convinces you)

• Do tests validate intent, not just implementation details?
• Is there at least one test for a failure path (unauthorized, invalid input, downstream error)?
• Are tricky parts covered with targeted unit tests, and integration tests where boundaries matter?
• If a bug fix, is there a regression test that fails without the fix?

A simple PR template that makes AI changes reviewable

A PR that says “Generated with AI” is not enough. What you want is a compact explanation that makes assumptions visible and makes testing reproducible. Here is a conceptual structure you can reuse:

Summary:
- What user/problem does this solve?

Design notes:
- Key assumptions:
- What is intentionally out of scope?

Risk:
- Tier (1/2/3):
- Security/privacy considerations:

Testing:
- Automated tests added/updated:
- Manual verification steps (if any):

Operational:
- Logging/metrics changes:
- Rollback plan (if needed):

This template is short on purpose. If a PR cannot fill it out, that is a clue the change is not yet understood well enough to merge.

Real-world example: AI-generated admin export feature

Imagine a small SaaS team wants an “Export customers to CSV” button for admins. A developer asks an assistant to draft the endpoint, query, and CSV formatting. It works in a local demo and looks tidy in the diff.

Using the checklist, a reviewer might catch issues that are easy to miss:

• Authorization: The endpoint checks “isAdmin” but not “admin of this account,” so a superuser token could export the wrong tenant if routing is misconfigured.
• Data minimization: The CSV includes internal notes and an “email_verified_at” field that is not needed for the export’s purpose.
• Performance: The export loads all customers into memory, which will break on large accounts and can take down the app if triggered repeatedly.
• Injection risk: The CSV is generated without guarding against spreadsheet formula injection for fields like name, which can begin with = or +.
• Testing gaps: The tests assert “a CSV is returned,” but not that it is scoped to the account, or that disallowed fields are excluded.

The fix is straightforward: scope queries by tenant, restrict fields, stream output or page through results, sanitize CSV cells defensively, and add tests that encode these guarantees. The assistant still helped, but the review made it safe.

Common mistakes to watch for

These are patterns reviewers can learn to recognize quickly. They are not “AI-only” bugs, but AI assistance makes them more frequent because code appears complete even when it is not.

• Implicit defaults: Accepting a library’s default timeout, retry policy, or TLS settings without checking if it matches production needs.
• False confidence from verbose code: More lines, more helper functions, and more comments can hide the fact that the behavior is still underspecified.
• Missing negative tests: No tests for unauthorized access, invalid input, or downstream failures.
• Inconsistent error handling: One path throws exceptions, another returns null, a third returns a partial response.
• Logging too much: Debug logs that include payloads, identifiers, or tokens that should never leave the boundary.
• “Looks like our architecture” but is not: A new service layer that duplicates an existing one, or a new pattern introduced because it is common on the internet, not because it is common in your codebase.

When NOT to accept AI-generated code

Sometimes the right call is to pause and rework the change, even if it “works.” Use this short stop list:

• High-risk surfaces without deep understanding: If nobody on the team can explain the change clearly, it is not ready, especially for Tier 3 areas.
• Unreviewable diffs: Large AI dumps with sweeping refactors make it hard to prove correctness. Ask for smaller, staged PRs.
• New dependencies to solve small problems: If a change adds a library to avoid writing 30 lines, the long-term cost often outweighs the short-term benefit.
• Security boundaries are “assumed”: If the PR relies on “the gateway handles auth” or “the UI prevents that,” require server-side enforcement.
• Tests are missing and hard to add: That often means the design needs to change to become testable.

Rejecting or reworking a PR is not a failure. It is an investment in a codebase that stays understandable when the original context is gone.

FAQ

Should we require authors to disclose AI use in PRs?

It can help, but disclosure alone is not a control. A better approach is to require the same structured evidence for any change, and then apply extra scrutiny based on risk tier and unfamiliar patterns in the diff.

How strict should we be about tests for AI-generated changes?

Match strictness to risk. For Tier 1, a lightweight unit test update may be enough. For Tier 2 and Tier 3, require at least one failure-path test and one test that captures a key invariant (tenant scoping, authorization, data shape, or idempotency).

What if the assistant’s solution is correct but doesn’t match our style?

Prefer consistency. Long-term maintenance costs usually come from variability. Ask for alignment with existing patterns unless there is a clear reason to introduce something new, and capture that reason in the PR description.

How do we keep reviews fast while using this checklist?

Make the checklist the author’s responsibility first: they should provide intent, risk tier, and test evidence. Reviewers then confirm, rather than discover, the critical details. Over time, this tends to reduce back-and-forth.

Conclusion

AI can accelerate implementation, but it does not remove the need for engineering judgment. A lightweight, repeatable review checklist turns “plausible code” into “trusted code” by focusing attention on intent, risk, security, and evidence.

If you adopt just two habits, make them these: always tier the risk, and always require a short PR write-up that explains assumptions and testing. Your future self, and your on-call rotation, will notice the difference.