Learn how to turn one giant AI-generated pull request into a clean, reviewable s

GitHub's stacked-review walkthrough organizes a large AI-generated change into smaller pull requests ordered by dependency. In its shopping-assistant example, the catalog data layer comes first, followed by a search API, chat answers grounded in its results, and the user interface. Each review can focus on one concern while retaining the preceding work it depends on.

The maintenance step matters as much as the initial split. When feedback changes a lower layer, the stack tooling rebases and synchronizes the dependent branches, and checks run again. The workflow makes the dependency chain explicit so reviewers can work from foundations upward without manually maintaining every branch relationship.