Your Teammate Generated the Feature. Now You Have to Review It.

Consider a hypothetical user story: a workspace administrator needs to export project activity as a CSV file. A teammate uses AI to build it, opens a pull request in GitHub, and asks you to review.

The download works. The tests pass. But you haven't yet established whether someone could export another workspace's data.

You need to trace the permissions, understand the query, and check what those tests actually cover. You weren't part of the decisions that produced this implementation.

You want to help your teammate ship. You also don't want to approve something you can't explain.

This is the part of AI-assisted development that bothers me. Producing the feature and understanding it well enough to approve it are different jobs. Making the first faster does not tell us how much work remains for the second.

I want AI to help us deliver software we understand and can stand behind. That means making room for both engineers' work, not treating the reviewer's attention as an unlimited resource.

01

One story. Two engineers’ work.

Author

Implement
and self-verify

Understand the proposal. Check the behavior, including what the AI produced.

Hand over context

Behavior, decisions,
evidence, unknowns

Reviewer

Understand
and inspect

Use the context to form an independent view and challenge its assumptions.

↺

Review findings → author corrections → reviewer recheck

Shared outcome

A completed story the team understands

The author may have done careful work. AI-generated does not automatically mean careless or incorrect. Even a well-prepared change still needs another person's judgment.

Here is the handoff I would test. We'll stay with the hypothetical export feature throughout; this is a proposed approach, not a report of results from my team.

Start before the whole feature exists

For this export, the consequential decisions include who may request it, which workspace's data is selected, and what happens when it fails. Discovering a disagreement about those rules after the interface, API, and query are all written could mean revisiting all three.

Bring the reviewer into those decisions early. Agree on the next checkpoint: perhaps a short design discussion or the first working path through the system. Give feedback while it can still change the direction.

That consumes the reviewer's time too. Reserve the conversation for meaningful uncertainty and include it in planning; a familiar, low-risk change may need much less coordination. Shopify's account of early draft-PR feedback offers a practice to borrow, not a measured result for this AI-assisted workflow. Shopify's engineering account.

Before requesting approval, the author still needs to inspect and verify the generated implementation. A useful handoff explains the important decisions, links to checks the reviewer can inspect, and names unresolved questions. It doesn't need to reproduce the prompt history. GitHub similarly recommends author self-review and contextual annotations before requesting review. GitHub's review guidance.

Saying “I'm unsure about this access rule” gives a teammate somewhere useful to begin. A confident generated description that nobody has checked does not.

Make the story reviewable

One user story does not have to arrive as one uninterrupted diff. For our export, the two engineers could agree on these connected review units before implementation:

02

Review the dependencies in order

1. 1

   Export rules + data selection

   Permitted workspace, fields, exclusions; tests.

2. 2

   Request + download

   Access boundaries, errors; integration tests.

3. 3

   Interface + user journey

   Controls, success, failure, completed download.

↳Upstream changes: revisit dependent pieces.

Together on the combined revision

AuthorVerify the complete feature.

+

ReviewerCheck interactions and acceptance criteria.

The reviewer needs the whole-feature map before inspecting the pieces. Each unit should carry its relevant tests and make its dependencies explicit; it need not be independently deployable. On the combined revision, explicitly recheck cross-workspace access and failure cases. Required specialist and code-owner approvals still apply.

This borrows from established practices: Meta describes dependent, individually reviewed changes, and Google recommends coherent scope with relevant tests. GitHub's August 2026 walkthrough applies stacking to a whole AI-generated feature. These are useful guides, not proof of a particular time saving. Meta on stacked changes, Google on coherent changes, GitHub's feature-review walkthrough.

Don't force tightly coupled behavior into fragments just to meet a line limit. If the full feature is already in one PR, map its decisions and dependencies together, then decide whether splitting it is safe or whether it needs a planned review in stages. Both take work.

Let the demo raise better questions

Back at the export, a successful download is a useful starting point. It is not yet an answer to the concern that brought you into the code.

03

A download is one piece of evidence

CSV

Download completed

The visible happy path.

Still to establish

Permitted person?

Inspect access checks; test who may export.

Correct workspace data?

Trace selection code; test cross-workspace access.

Failure handled?

Exercise errors; inspect recovery behavior and failure tests.

Link findings to code, tests, and the reviewed revision.

A short author-led walkthrough can explain the intended behavior, the path through the code, and the choices worth challenging. Then the reviewer needs time to inspect independently, choose failure cases, and question the explanation—including the acceptance criteria themselves.

The goal is not to persuade the reviewer to agree. It is to give them enough context to make their own judgment. Record the important decisions and the revision examined so that context survives the call.

If they still cannot understand the change well enough to approve it, clarify the proposal, reduce its scope, or arrange qualified help. Fast implementation should not become pressure to approve.

Don't make every correction a fresh mystery

Suppose the review finds that the export's permission check needs to change. The author asks the AI to fix it, but the resulting update also changes the data-selection model.

Now the earlier reasoning about which records belong in the export needs another look.

For each correction round, identify what changed since the reviewed revision, which concern it addresses, and which checks were rerun. The author must verify the actual diff, not just pass along another generated summary. Keep unrelated cleanup out of the round.

Sometimes a finding really does require redesign. Say so and agree on the scope of fresh review. An upstream change can invalidate downstream assumptions; an old approval does not cover every later version of the feature.

Judge the change at team level

Try this handoff on a few comparable stories in one team, with a checkpoint after two weeks. Include all PRs belonging to each story and any work still unfinished.

Keep a lightweight record of author preparation, reviewer orientation and inspection, correction rounds, and waiting. Count both people's time in early conversations. That helps distinguish a genuine improvement from moving effort out of one person's column and into another's.

Ask whether the reviewer could explain the important behavior and remaining risks. Follow defects and corrective work after release too. A short trial can expose friction; it cannot establish long-term safety.

AI review tools may help with this work. Assess them by verified useful findings and the attention they consume, not the number of comments they generate. Neither an automated review nor a generated summary removes the two engineers' responsibilities.

As a manager, I would not rank engineers by review speed or comment count. Finding a consequential problem contributes to delivery, even when the PR stays open. The team needs time for review, clear ownership, sensible work in progress, and support when someone encounters unfamiliar code.

For our export, success means more than a file downloading. The author can explain the implementation, the reviewer has checked the important boundaries, and the team can take responsibility for what it ships. That is the kind of progress I want AI to help us make.

What the research can—and can't—tell us

The proposals above are informed by practitioner guidance, but the empirical evidence does not directly measure this exact two-engineer handoff.

One study of agent-authored GitHub PRs links structural size and breadth to review-interaction volume. Its measure is comments and review events, not time spent understanding code. Another examines how human comments mix evaluation with directions to agents. Both use overlapping public PR data; neither establishes that AI authorship itself causes poor review or tells us what this workflow costs a particular team. Review-effort study, human-review study.

Google's older AutoCommenter experiment found no statistically significant change in review duration or active review time. Its limited best-practice scope makes it unsuitable for judging all current tools. It does illustrate why useful automated feedback and improved human workflow are different outcomes. AutoCommenter study.

Research checked September 6, 2026. The diagrams describe a proposed process and a hypothetical feature, not measured timings, a real product screenshot, or a promised result.