Code Review Workflows for Teams Using AI
Adapt your team's code review to a world of larger, faster, AI assisted pull requests
A taste of a lesson
Since adopting coding agents, our pull requests average 1,200 lines and reviews take days. What should we change first?
Start with size, because everything else depends on it. Agree a guideline, such as a few hundred changed lines, and ask authors to split work into a sequence of pull requests, with mechanical changes labelled separately. Ask agents for smaller tasks in the first place. Require descriptions that say what to look at closely. Then make sure CI catches formatting, types and tests before a human looks. After a month, compare review times and reverts. How do your developers currently brief the agents: one big task, or several small ones?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Set pull request size and description standards suited to AI assisted work
- Make authors accountable for understanding and explaining every change
- Use automated checks and AI review tools as first passes, not approvals
- Assign code owners and checklists for sensitive areas
- Track review health metrics without ranking individuals
Lesson plan
- 1 What changes when code gets cheaper Understand how AI assistance shifts load and risk to code review. Start
- 2 Pull request standards Set size and description rules that make review possible. Start
- 3 Automation and AI reviewers Let machines catch what they can before humans review. Start
- 4 Checklists and code owners Focus human attention where risk is highest. Start
- 5 Disclosure and team norms Agree how and whether to note AI assistance, without blame. Start
- 6 Measuring and improving review Track review health and feed lessons back into the workflow. Start
Try asking
About this tutor
For tech leads, engineering managers and senior developers whose teams now produce more code with assistants and agents. Review becomes the bottleneck and the main quality control. This tutor covers setting pull request size limits, requiring authors to understand and explain their changes, disclosure norms, using AI review tools as a first pass without replacing human judgment, review checklists for generated code, automated checks in CI, ownership of risky areas, and measuring review health. You will design a review policy your team can adopt.
Reviews
4.5
2 ratingsSample
- Sunita R.Sample
Clear policies with reasons behind them. We adopted the checklist and code owner rules almost word for word.
- Martin E.Sample
The accountability framing ended a tense debate in our team about AI use. Size guidelines cut our review times noticeably.
About the teacher
I teach how to review, test, refactor and secure code written with AI help
9 tutors 320 lessons taught Sample
I care about what happens after the code is generated. My background is in code review, testing and application security, and I teach developers to treat AI output as a draft from a fast, confident colleague who has never seen production. We practise reading diffs carefully, writing tests before asking for code, refactoring old systems in safe steps and spotting...
See Ilse's profile and tutorsMore like this
Other tutors on the same or nearby topics.