In the fast-paced world of software development, AI-generated code has become a game-changer—but it also introduces a new challenge: managing massive, monolithic pull requests (PRs). A single AI-generated PR can contain hundreds of files and thousands of lines of changes, making it nearly impossible to review effectively. This article explores a practical strategy to transform such a giant PR into a reviewable stack, ensuring that your team can maintain code quality and productivity.
The Problem with Giant AI-Generated PRs
AI tools, such as GitHub Copilot, can produce vast amounts of code in a single session. While this accelerates development, it often results in PRs that are unwieldy and difficult to review. Reviewers face cognitive overload, increased error rates, and longer review cycles. Moreover, large PRs are more likely to be delayed, creating bottlenecks in the development pipeline.
The Solution: Splitting into a Stack
A stack is a series of smaller, interdependent PRs that build on each other. By breaking down a giant PR into a logical sequence of changes, you can review each part in manageable chunks. Here’s how to do it effectively:
1. **Identify Logical Units of Change**
Start by analyzing the AI-generated code. Look for natural boundaries such as feature additions, refactoring, bug fixes, or documentation updates. Group related changes into coherent units. For example, if the PR adds a new API endpoint, separates the data model, the business logic, and the tests into distinct groups.
2. **Order the Stack**
Arrange the units in a dependency order. The base PR should contain the foundational changes that others depend on. Each subsequent PR should build upon the previous one, ensuring that each stack entry is compilable and testable on its own. This allows reviewers to understand the evolution of the codebase step by step.
3. **Create a Stacking Workflow**
Use a tool that supports stacked PRs, such as git stack or GitHub’s built-in stack support (which, by 2026, has become more mature). Many teams now leverage tools like Graphite or git-branchstack to manage stacks directly from the terminal. Alternatively, you can manually create branches and PRs, but automation reduces friction.
4. **Communicate the Stack**
Clearly document the stack in each PR description. Reference upstream and downstream PRs, and explain dependencies. This helps reviewers understand the context and review in the correct order.
5. **Iterate and Merge**
Review and merge the stack incrementally. Once the base PR is approved and merged, rebase subsequent PRs onto the updated main branch. This keeps the stack in sync and minimizes merge conflicts.
Benefits of Stacked PRs
- Improved Reviewability: Smaller PRs are easier to understand, leading to faster and more thorough reviews.
- Reduced Cognitive Load: Reviewers can focus on one logical change at a time, reducing fatigue and increasing accuracy.
- Faster Feedback: You can get feedback on early parts of the work while later parts are still in progress.
- Better History: The commit history becomes cleaner and more meaningful, which is invaluable for future maintenance.
Practical Example
Imagine you used Copilot to generate a full-stack feature that includes database migrations, API endpoints, frontend components, and tests. Instead of submitting one PR, you might create a stack like this:
- PR 1: Database schema and migration
- PR 2: API endpoints and business logic
- PR 3: Frontend UI components
- PR 4: Tests and documentation
Each PR is reviewable independently, and dependencies are clear.
Conclusion
AI-generated code is here to stay, and it’s getting more powerful every year. By turning a giant AI-generated pull request into a reviewable stack, you can harness the speed of AI without sacrificing code quality. This approach not only improves the review experience but also sets your team up for long-term maintainability. Start stacking your next AI-generated PR and watch your review cycle become smoother and more efficient.
via GitHub AI Blog