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

The ability to transform unwieldy, AI-generated code into manageable, reviewable segments offers a significant opportunity for any team embracing automated development. This piece from GitHub Engineering describes how to teach AI coding agents to decompose large changes, specifically those stemming from AI assistance, into a clean, ordered stack of smaller, interconnected pull requests. The core idea is to leverage GitHub’s stacked pull requests feature to break down monolithic code contributions into a series of logical, digestible steps, making the review process much more efficient and less error-prone. This approach profoundly affects developers and team leads alike, streamlining workflows and enhancing code quality. Consider a small e-commerce shop in Bulawayo, "Zambezi Crafts," that recently integrated an AI assistant to generate new product catalog features. Instead of receiving a single, sprawling pull request that’s overwhelming for their two-person development team to review, the AI is now trained to submit changes as a stack: first, the database schema update; then, the API endpoint for fetching new products; finally, the frontend UI components. This allows their lead developer to review each layer independently, accelerating deployment and reducing bugs. Similarly, an independent SaaS founder in Harare building a logistics platform, "Chita Express," can use this technique to manage contributions from their AI co-developer. If the AI proposes a significant refactor to optimize route planning, breaking it into a stack—like core algorithm adjustments, then caching improvements, then integration tests—ensures the founder can incrementally validate each change, maintaining stability for their early adopters. Even an internal IT team at a mid-size mining company near Zvishavane could benefit; when their AI helps automate reporting features, stacked pull requests mean the human team can verify data integrity changes before UI updates, minimizing disruption to critical operations. To experiment with this concept, select a recent, moderately sized AI-generated code change in your own development environment that ideally involves several distinct logical steps. This week, try manually breaking that single large pull request into a series of 2-3 smaller, interdependent pull requests, mimicking the "stacked" approach described. Observe the difference in your own review time and the perceived complexity compared to reviewing it as one large chunk.