What is a pull request: a beginner's guide to PRs, code review, and why AI-generated PRs matter

A pull request is a version control feature that lets developers propose code changes to a repository and request review before merging to the main branch.

It is the primary quality gate in modern software development: the point where bugs are caught, knowledge is shared, and code standards are enforced. With AI coding assistants generating an increasing share of production code, understanding what a pull request is and how to review one carefully has become a core engineering skill, not just a workflow step.

Most developers encounter pull requests within their first week at a new job. The mechanics are straightforward: create a branch, write code, open a pull request, get review, merge. But the purpose goes beyond mechanics.

It is a cultural practice that says: "No code reaches production without someone else understanding what it does and why." In teams using AI tools, that practice is more critical than ever because AI-generated code looks polished but can contain subtle logic errors that only a human reviewer will catch.

What is a pull request in version control?

A pull request (also called a merge request in GitLab) is a feature of Git-based version control systems that allows a developer to propose changes from one branch to another, typically from a feature branch to the main branch. It creates a dedicated space for code review, discussion, and approval.

The pull request workflow in six steps

1. Create a feature branch: branch from the latest origin branch so you start from current code, and keep your changes isolated from production code.
2. Write code and commit: save your changes to the feature branch in logical units, and run the test suite locally so the branch is verified before anyone else sees it.
3. Open a pull request: read through your own diff first to catch typos and leftover debug code, then open the request with a description explaining what the change does and why.
4. Code review and feedback: peer reviewers examine the code and leave comments or request changes.
5. CI checks pass: automated tests and linting must pass before merging.
6. Merge to main branch: once approved, the code is integrated and deployed.

The pull request is not the merge itself; it is the review process that precedes it. The value is found in the review, not the final button click.

Why do pull requests matter beyond just merging code?

Pull requests serve four purposes that make them indispensable to engineering teams:

• Quality gate: the last human checkpoint before production. Reviewers catch bugs and security vulnerabilities.
• Knowledge sharing: distributes institutional knowledge so that the team understands the system even if the original author leaves.
• Documentation: serves as a permanent record of why a change was made, which is invaluable for future maintenance.
• Standards enforcement: ensures architectural patterns and security practices are followed consistently.

How to write a good pull request

A good pull request makes the reviewer's job easier, which shortens the review cycle.

• Keep it small: a SmartBear study of a Cisco Systems engineering team found that reviewers should look at no more than 200 to 400 lines of code at a time, because defect detection drops off beyond that.
• Describe the "why": the code shows what changed; the description must explain the problem being solved and the trade-offs made.
• Include test coverage: every logic change should include tests to verify the behaviour.
• Self-review first: catch obvious typos and debug logs yourself to save the reviewer's cognitive budget for architecture and logic.

Why AI-generated pull requests change everything

AI coding assistants generate code faster than humans, which fundamentally changes the review dynamic.

• Increased volume: AI tools make it easy to put a large amount of change into a single PR, and larger PRs tend to receive less thorough review, which creates a quality risk.
• The "polished" illusion: AI produces syntactically correct code that looks professional. Reviewers often skim it faster, assuming correctness, but the logic may miss business requirements.
• The attribution gap: if an engineer uses AI to generate code, they may not understand every implementation detail. The reviewer becomes the final line of defence against bugs the author might not have fully grasped.

How should teams adapt their pull request process for AI?

Three practical changes can maintain safety without sacrificing the speed of AI tools:

1. Enforce PR size limits: set a soft limit of 400 lines. Break larger AI-generated changes into multiple PRs.
2. Require AI context: developers should note which sections were AI-assisted so reviewers know where to focus their attention for edge cases.
3. Add AI-specific review checks: check if the generated code handles edge cases from the original requirement and ensure it doesn't make undocumented assumptions.

Tetriz is building the measurement layer that tracks pull request review quality alongside delivery speed, giving engineering managers visibility into whether code review is keeping pace with AI-generated code volume. Join the early-access cohort to measure what your PR dashboards cannot.