Code Review Automation
Code Review Automation Lab
A hands-on lab for building AI-assisted review checks that support human judgment rather than replacing it.
₩14,500,000 · 4 weeks · Private team cohort
Program description
Teams define review categories, design assistant prompts, test false positives, and decide what belongs in automated comments versus human discussion. The lab ends with a pilot-ready code review workflow.
Best fit: 10-24 people, advanced level, focused on code review.
Responsible lead
Soojin Lee
Lead AI Integration Coach focused on review systems and engineering productivity.
Included features
- Review taxonomy design
- AI comment quality rubric
- Pull request scenario testing
- False-positive triage
- Reviewer handoff playbook
- Pilot measurement worksheet
Implementation outcomes
- A pilot code review assistant workflow
- Reviewer escalation rules
- A measurement plan for comment quality
Questions teams ask
Can we use our own repository and workflow data?
Yes. Teams can bring sanitized examples, internal process notes, or non-sensitive pull requests. We do not require access to production systems.
What is not included?
The bootcamp does not replace security review, legal review, or procurement approval for a new AI vendor. It gives your team the working practices and rollout artifacts to support those decisions.
How much engineering time is expected?
Most teams reserve two half-days for live sessions and three to five hours for implementation labs between sessions.
Participant notes
The false-positive session kept us honest. We left with fewer automations than planned, which was exactly the point.
Minho Chae, Tech Lead, Commerce API team
Clear, technical, and specific to pull request review.
Verified participant