Most code review best practices articles agree on the basics: keep pull requests small, respond fast, be constructive in comments. None of them tell a CTO or VP Eng what to actually put in a written policy once half the team works nine time zones away, or a chunk of a typical diff came from an AI assistant instead of a person. That's a different problem than the one Google's engineering guide or the usual tips list solves.
A lead has four decisions to make once and write down: how big a pull request gets before it's rejected, how fast a reviewer has to respond given the team's real overlap hours, what changes in the checklist when a PR is AI-assisted, and who gets full sign-off authority once the team includes senior engineers who aren't on the company's payroll.
Why generic code review checklists break on a blended team
Most of what ranks for this topic reads like it was written for one engineer reviewing another engineer's pull request, both employees of the same company, sitting in the same office. Google's own engineering practices guide is a good example: it splits into a reviewer's guide and an author's guide, both walking through how to run a single review, not how to set policy for a whole team. That's a useful document. It's also silent on the questions a lead actually needs answered.
The gap widens once the team stops matching what these guides assume. A team with a nearshore engineer in Krakow, an augmented senior in Belgrade, and a junior generating a third of their diff with an AI assistant needs answers none of the generic lists provide: how big can a PR get before review quality drops, how fast does a reviewer on a different clock have to respond, does a senior contractor get full sign-off authority, and what does a reviewer actually check differently when part of a change wasn't typed by a person. Answer those four once, in writing, instead of arguing them out pull request by pull request.
How to set code review standards for a distributed engineering team
1. Set a PR size ceiling and defend it
Google's guidelines on change size put a number on it: "100 lines is usually a reasonable size for a CL, and 1000 lines is usually too large, but it's up to the judgment of your reviewer." The reasoning holds up as well as the number: "It's easier for a reviewer to find five minutes several times to review small CLs than to set aside a 30 minute block to review one large CL."
Industry data backs a similar range from a different angle. SmartBear's peer code review research associates the highest defect-discovery rate with reviews that stay under roughly 400 lines at a time, at under 500 lines an hour, in sessions capped near 60 minutes. Push past those thresholds and the rate of defects actually caught drops sharply. Atlassian's code review guide cites a Cisco study for a comparable window: most defects are found in the first 200 lines, and reviewing more than 400 lines at once hurts a reviewer's ability to find bugs.
Put a number in the policy, not a guideline. 400 lines is a reasonable hard ceiling for a single PR, with an explicit exception path, a migration, a generated-file diff, that requires a second reviewer's sign-off instead of a quiet override. A ceiling nobody enforces doesn't function as a standard.
2. Set a review-turnaround SLA around your team's real timezone overlap
Google's guidance on reviewer response time sets the floor: "One business day is the maximum time it should take to respond to a code review request (i.e., first thing the next morning)." That's a reasonable default for a single-office team. It breaks the moment "next morning" belongs to two different clocks.
A CET nearshore engineer and a UK-based reviewer clear roughly eight hours of daily overlap; a CET-to-US-East pairing gets closer to three. Inside those windows, one business day is realistic. Outside them, "next morning" turns into the same failure mode HighCircl has already documented in nearshore delivery: a two-hour PR review turning into an overnight wait, with a sprint's velocity eroding review by review. Write the SLA against actual overlap hours instead of a blanket 24-hour clock: same-day response inside the overlap window, next business morning outside it, with an explicit escalation path (see step 5) for anything still unresolved after two rounds of comments.
The same guidance states the principle behind both standards plainly: "At Google, we optimize for the speed at which a team of developers can produce a product together, as opposed to optimizing for the speed at which an individual developer can write code." A policy that lets one senior batch a week's work into an 800-line PR is optimizing for that person's convenience at the team's expense.
3. Write down what counts as "AI-generated" for review purposes
None of the guides ranking for this topic say anything about AI-generated code, which is strange given how much of a typical diff now includes it. Start with a written definition: a PR counts as AI-assisted for review purposes once an AI tool generated more than a trivial share of the diff, whatever threshold the team picks, and the PR description has to say so. Without that flag, a reviewer has no way to know which parts of a change need extra scrutiny and which parts a human wrote and already understands cold.
The scrutiny matters for a reason that isn't hypothetical. DORA's 2024 report found that AI adoption raised individual productivity, flow, and job satisfaction, while it lowered software delivery stability and throughput at the team level. DORA doesn't explain the mechanism. Read against how code actually gets produced now, the likely explanation is straightforward: engineers ship more code per hour with an assistant, and without matching investment elsewhere in the pipeline, that's why review capacity becomes the bottleneck instead of the code getting written faster end to end. That reading is HighCircl's own inference from the finding, not a claim DORA makes itself, and it's the reason a review policy that ignores AI-assisted volume is already out of date.
Once a PR is flagged, the reviewer's job changes shape. Correctness still gets checked, but it stops being the main question. The main question becomes what the assistant produced that looks right and isn't, and whether a human author would have made the same mistake.
4. Decide who holds review authority when the team is blended
Once a senior engineer isn't on the company's payroll, who holds review authority in a blended team stops being obvious, and none of the single-employer guides answer it. HighCircl's position: "a senior augmented engineer should have full voting weight in code review and architecture discussions, the same as any senior in-house engineer." Restricting a strong senior to comment-only status because of how they're employed wastes the judgment a company is paying for.
The exception is decisions with a lifespan longer than a typical engagement: a core data model, an infrastructure commitment a team will live with for years. Those still need an in-house senior's sign-off alongside the augmented engineer's. The reason is accountability: the augmented engineer won't be the one answering for it eighteen months out. Write both rules into the same document that sets PR size and turnaround, so nobody has to guess mid-review whether a given approval counts.
5. Route what won't resolve in comments to a synchronous call
Some threads stop being productive around the third round of back-and-forth. Stack Overflow's guide to cross-office code reviews recommends scheduling overlapping-hours windows specifically for review-heavy work, and escalating any comment-heavy thread to a synchronous conversation rather than letting it run indefinitely in writing.
Put a number on the escalation trigger so it isn't a judgment call made under frustration: two rounds of unresolved comments on the same file, or a thread still open a day past the SLA from step 2, triggers a 15-minute call instead of a third round of comments. The call doesn't replace the written review. Whatever gets decided on it goes back into the PR thread as a comment, so the record stays in one place.
What actually changes when you're reviewing AI-generated code
Three checks matter more here than for hand-written code.
Dependency and API existence deserve a first look. AI assistants occasionally suggest a package, a method, or a config flag that doesn't exist, or existed in an older version than the one a team actually runs. A person writing from memory rarely does this, because they only reach for things they've used before. Confirm that every import and API call in a flagged PR resolves against the versions the codebase runs.
Edge cases the assistant didn't reason about come next. Generated code often handles the happy path cleanly and skips the case a human author would have caught by thinking through the business logic: an empty list, a null field, a retry after a timeout. The code compiles and passes the obvious test. It just doesn't handle the case nobody typed a prompt for.
License and provenance apply to anything that looks copied rather than composed. A block of code that reads like it came from a specific open source project, rather than something an assistant generated fresh, is worth a second look at where it actually came from and what license attaches to it.
None of this replaces the standards in steps 1 and 2. A flagged PR still has to clear the size ceiling and the turnaround SLA. These three checks sit on top of the usual ones, not instead of them.
Frequently asked questions
How many lines of code should a single review cover?
Google's own guidance treats 100 lines as a reasonable size and 1000 as too large, framed around author discipline: keep changes small so review happens in short sessions instead of one long block. Atlassian and SmartBear come at the same number from the reviewer's side. Defect-detection drops sharply once a single sitting covers much more than 200-400 lines, regardless of how disciplined the author was. Both point to a similar ceiling for different reasons.
How fast should a code review turn around?
One business day is Google's floor for a single-office team. Across time zones, that floor only holds inside the team's actual overlap window. Same-day response works fine for a CET-UK pairing with eight hours of shared working time; a CET-US East pairing with roughly three hours of overlap needs an explicit next-business-morning standard instead of an assumed 24-hour clock.
Does a senior nearshore or augmented engineer get full code review authority?
Yes, for day-to-day review and architecture discussion. HighCircl's stated position is full voting weight for a senior augmented engineer, the same as any in-house senior. The one carve-out is decisions with a multi-year lifespan, like a core data model, where an in-house senior's sign-off sits alongside the augmented engineer's rather than replacing it.
Should AI-generated code go through a different review process than human-written code?
The PR-level standards stay the same: same size ceiling, same turnaround SLA. What changes is the checklist once a PR is flagged as AI-assisted. Confirm that suggested dependencies and APIs actually exist, look specifically for edge cases the assistant wouldn't have reasoned through, and treat anything that looks copied rather than generated as a license question worth a second look.