3 ways to streamline AI code reviews across your team
GitHub outlines ways to prevent pull request backlogs from growing as AI coding tools increase developer output, and explains how to recalibrate review practices for agentic development.
Overview
The video explains why review fatigue can happen when AI-assisted coding increases the volume of incoming pull requests, and presents three workflow adjustments to streamline reviews across a team:
1) Improve prompt inputs
- Emphasizes improving the quality of prompts and inputs given to AI coding tools so the resulting changes are easier to review.
- The goal is to reduce avoidable churn in PRs by getting more targeted, higher-signal changes from the start.
2) Keep pull requests small with stacked PRs
- Recommends keeping PRs smaller to make reviews faster and less draining.
- Suggests using stacked pull requests as a technique to break large changes into a sequence of smaller, reviewable units.
- Reference: Stacked Pull Requests: https://gh.io/gh-stack
3) Shift focus toward assessment
- Encourages shifting review effort toward assessing the change (intent, correctness, risk) rather than spending most time on manual editing.
- Frames this as a better fit for agentic development workflows where more code is produced per developer.
Related links
- GitHub's Agentic Engineering System: http://gh.io/aes-framework
- Stacked Pull Requests: https://gh.io/gh-stack