Code Review Standards for AI-Assisted Development

The instinct on a lot of teams is to treat AI-generated code like any other PR — same checklist, same review depth. That's not wrong exactly, but it misses where the actual risk concentrates. AI-assisted code fails differently than hand-written code, and review needs to be aimed at those specific failure modes, not just applied at the same intensity.

Where AI-generated code actually goes wrong

Not randomly — in specific, recurring patterns:

• Plausible-looking code that's subtly wrong. A model produces syntactically correct, idiomatic-looking code that handles the common case and silently mishandles an edge case a human author would have paused on.
• Confident hallucination of APIs or behavior. Code that calls a method that doesn't exist, or assumes a library behaves differently than it does — wrong in a way that often doesn't fail until runtime, sometimes not until production.
• Context loss across a large generation. The further a single generation drifts from the immediate prompt, the more likely it silently contradicts an assumption made elsewhere in the codebase.
• Security-relevant shortcuts. Models trained broadly tend toward the most common pattern, not the most secure one — input validation, auth checks, and injection protection are common places this shows up.

None of these are review failures unique to AI-assisted code — humans make similar mistakes. What's different is the frequency and shape of the mistakes, which means review should look for them specifically, not just generally.

Our standard: review depth matches generation surface, not authorship

The rule isn't "review AI code harder than human code." It's: review scrutiny scales with how much of the change was generated versus authored, and how far the generation drifted from a narrow, well-specified prompt.

In practice:

• Boilerplate and well-understood patterns (CRUD scaffolding, test fixtures, standard config): light review, same as equivalent hand-written boilerplate would get.
• Business logic and integration code, whether AI-assisted or not: full review — every branch, every edge case, traced by a human who understands the domain.
• Anything security-sensitive, architecture-defining, or touching data governance: human-authored or human-substantially-rewritten, always, regardless of what generated the first draft. This is a hard line, not a guideline (see our engineering standards overview for how this fits the broader standard).

A review checklist specific to AI-assisted PRs

Beyond normal review criteria, every AI-assisted PR gets checked against:

1. Can the author explain every line without re-prompting the model? If not, it's not ready for review yet.
2. Were edge cases considered, or just the happy path? Ask directly — "what happens if this input is empty / this call fails / this runs concurrently" — and expect a real answer, not a re-generation.
3. Does every external API call, library method, and function signature actually exist and behave as used? Verified, not assumed.
4. Does this change introduce or touch anything security-sensitive (auth, input handling, data access)? If yes, it gets the human-authored standard above, retroactively if needed.
5. Does it contradict an assumption made elsewhere in the codebase that a narrower context window wouldn't have seen? This is the reviewer's job specifically — the model can't check this for itself.

Why this isn't optional

A pod that generates fast but reviews loosely produces velocity that evaporates the first time a subtly-wrong assumption reaches production. The velocity gain from AI-assisted development is real, but it's a velocity gain in generation, not in the review step — and treating it as a shortcut in review is where teams actually get burned. See our guide to evaluating AI-native engineering talent for how we screen for engineers who already hold this line by instinct, before they ever touch one of our pods.