Turn one giant AI-generated pull request to a reviewable stack

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Think about the last big feature you shipped. Be honest. Did you cram it into one giant pull request, or did you split it into smaller scoped pull requests? For years, you have silently had to decide between watching a pull request grow so large that reviewing it becomes a nightmare or breaking it into a chain of smaller pull requests that you have to babysit, sync by hand, and untangle conflicts every time a change is introduced below.

Both options have trade-offs. One is hard to review, while the other is hard to maintain. Your decision that day leans towards the less painful option.

Now add coding agents. They are incredibly productive and are projected to drive a 50% productivity gain across every SDLC stage by 2028, according to Gartner. But, they can’t take away the choice of how you structure your pull requests. They amplify the need to make it.

In this post, follow along with an example of how you can use stacked pull requests to simplify reviews.

A closer look: Adding product search to a shopping assistant

Let’s say you issue a prompt to add product search to a shopping assistant, walk away and minutes later, literally, you come back to review, steer, and approve. But look closely at what tends to land in that single pull request:

A new data model and its seed data

An API route and its validation

The client wiring and the UI and the empty/fallback/error states

…all of this and more in one ginormous 1,000+ line diff.

For agents largely trained on how code has traditionally been written over the years, this pattern is their default way of shipping. …

Vollständiger Originalartikel ↗

• Betroffenheit im eigenen Stack prüfen: Versionen/Komponenten abgleichen.
• Originalquelle / Hersteller-Advisory lesen: https://github.blog/engineering/turn-one-giant-ai-generated-pull-request-to-a-reviewable-stack/
• Verfügbaren Patch oder Workaround einspielen und dokumentieren.