GitHub uses stacked PRs to break giant AI-generated code into reviewable chunks

GitHub 如何用堆叠式 Pull Request 拆解 AI 生成的巨型代码

GitHub engineers share a workflow for taming AI-generated mega-PRs: after letting AI produce an entire feature in one shot, they use stacked PRs to automatically split thousands of lines into logical, independent chunks of 200–400 lines each. The core idea is to generate the full change first, then slice it into a stack based on file dependencies and semantics, so reviewers can focus on one concern per layer. The post includes concrete commands and branch-naming conventions, but doesn't disclose internal adoption rates or review-time comparisons.

Why it matters: GitHub's official engineering blog shares a hands-on workflow for handling large AI-generated code blocks, with concrete commands and splitting logic that teams using AI for coding can directly reference. But the lack of internal usage data and quantified review-time improveme...