What Is a PR in Software Development, and What AI Changed About It

If you work near an engineering team — as a developer, an engineering manager, or a founder — you've probably heard the term PR tossed around without ever getting a clean definition. A pull request (PR) is a formal request to merge one branch of code into another, on platforms like GitHub, GitLab, or Bitbucket.

A pull request lets teammates examine and comment on a change before it lands in the main branch.

In a typical PR lifecycle, an author opens the request, reviewers inspect the changes, and maintainers approve it or ask for revisions. CI/CD pipelines run unit tests, integration tests, and static analysis automatically to catch regressions and style issues before a human ever looks at the diff.

Increasingly, AI tools add automated review comments and suggestions of their own — flagging potential bugs, proposing fixes, or suggesting refactors. That changes what a reviewer actually needs to check, which is the second half of this piece.

Key Takeaways

• A pull request (PR) is a formal proposal to merge code changes from one branch into another, used on platforms such as GitHub, GitLab, and Bitbucket. GitHub calls it a "Pull Request"; GitLab calls the same thing a "Merge Request." Both exist to force a structured review before new code reaches the main branch.
• PRs support collaboration: team members review code for bugs, suggest improvements, and keep a documented history of every change and the reasoning behind it.
• CI/CD pipelines — GitHub Actions among them — run tests automatically as soon as a PR is opened, catching regressions before a human reviewer looks at the diff.
• AI-assisted review tools now add their own suggestions during review, flagging likely bugs or proposing refactors, which can speed up approvals — but the PR description still has to explain what changed and why for a reviewer to evaluate it.
• Keeping PRs small and focused, writing a clear description, and linking the originating ticket all make both human and AI-assisted review faster.

What Is a Pull Request (PR) in Software Development?

A pull request, often called a PR, lets developers propose changes to the source code in a shared repository. Teams use PRs on platforms like Bitbucket or GitHub to start reviews and merge new features or bug fixes into the main project branch.

Definition and purpose of a PR

A pull request, or PR, is a formal request to merge one branch of code into another within a repository, on platforms like GitHub, GitLab, or Bitbucket. GitHub calls this a Pull Request; GitLab calls it a Merge Request — the workflow is a feature of those platforms, not a core Git command.

Opening a pull request means attaching the commits, a clear description of the change, and a reference to the ticket that prompted it.

The PR is where the code review process happens: teammates read the new code, suggest improvements, and check that the tests cover the change before it merges into the main branch. It's also the record — the description, the discussion, and the test results all stay attached to the change, which makes it possible to trace why a given line exists months later.

How PRs fit into the development workflow

A developer creates a pull request by branching off (or forking) the main repository, committing changes to that branch, and then opening the PR for review. Some teams manage several branches at once with git worktrees rather than switching a single working copy back and forth.

Small, focused PRs are easier to review well than large ones — a maintainer or reviewer can actually hold the whole change in their head. Maintainers decide whether a PR meets the bar to merge; reviewers examine it for correctness and quality and leave feedback.

Once reviewers approve, the change merges into the main branch. Git tracks the full history of branches, forks, and merges, so the sequence of changes stays visible long after the PR closes.

Benefits of Pull Requests

Pull requests enhance code quality by allowing developers to review changes collaboratively. They promote knowledge sharing among team members, ensuring everyone understands the proposed modifications and their impact on the project.

Enhances code quality and collaboration

Pull requests catch bugs and vulnerabilities before they reach production, because a second person is required to look at the change before it merges. That review step also spreads knowledge: reviewers learn what changed and why, and authors get feedback that improves the next PR.

Discussion happens directly on the PR — questions, feedback, and proposed edits all attach to the specific lines they concern, rather than living in a separate chat thread disconnected from the code.

Improves traceability and transparency

A PR is also a documented record: what changed, why, who approved it, and what the tests showed. A clear PR description — the problem, the approach, and how it was tested — makes that record useful later, not just at merge time.

Because reviewers can see the diff and the reasoning behind it, they can test the branch locally before approving, and anyone auditing the history later can see the same context.

Common Pitfalls and Best Practices in PR Workflows

Common pitfalls include vague PR descriptions, diffs so large that a reviewer can't hold the whole change in mind, and merging before automated tests finish. The fix is procedural: keep PRs small and scoped to one concern, write a description that states the problem and the approach, use a PR template so nothing gets left out, and let CI finish before merging.

How AI Is Changing Pull Requests

AI is changing how pull requests get reviewed. Automated tools now flag likely problems and suggest fixes before a human ever opens the diff, and CI/CD pipelines increasingly fold AI-assisted checks into the same automated gate that runs tests.

Automated code reviews using AI-powered tools

AI-powered review tools read a diff and flag likely bugs, security issues, or style problems automatically — the same way a linter does, but with more context about what the change is trying to do. Static analysis and test runs can now start the moment a PR opens and block the merge until they pass, catching problems earlier in the cycle than a human reviewer alone would. That is the same reasoning behind screening AI-generated code before it reaches main: the earlier a check runs, the cheaper the fix.

Intelligent suggestions for code improvements

AI tools can generate suggestions for a change during review — flagging a likely edge case or proposing an alternative approach — which gives reviewers a second opinion to weigh against their own read of the diff.

Automated systems can also produce larger and more complex diffs than a person typically would. Reviewers need to stay cautious, since AI-generated changes can misread the original intent even when the code looks plausible, and an automated reviewer can get the call wrong in either direction — flagging a real problem is only useful if a human still has the final say. That makes clear PR descriptions and titles more important, not less — they're what lets a reviewer evaluate an AI-authored change quickly.

Reevaluating PR reviews with AI-generated diffs

When an AI coding agent authors a diff, pull requests can include larger and less familiar changes than a person would normally submit. The code can look plausible even when it misreads the original intent.

That shifts what a reviewer has to check: confirming that tests actually pass and that the underlying logic matches the requirement matters more than checking style, because style is usually the one thing the agent gets right. The same shift is why agentic coding setups increasingly separate what an agent is allowed to touch from what still needs a human sign-off — see walls and rails for agentic coding.

Streamlined PR processes with CI/CD and AI integration

CI/CD pipelines — GitHub Actions and GitLab CI among them — run unit and integration tests automatically at every stage of a PR's lifecycle, which keeps code quality consistent without requiring a person to remember to run them.

Some teams manage several related changes at once through stacked PRs, keeping each one small and focused while a ticket-tracking tool like Jira follows the whole change through its stages. Static analysis inside the same CI pipeline can catch security issues, and even flag hardcoded secrets, before a PR is merged.

Conclusion

Pull requests exist to put a second set of eyes on a change before it reaches the main branch — that's the whole mechanism, whether the code was written by a person or an AI agent. AI tools now add their own layer of automated review and suggestion on top of that, which speeds up approvals but doesn't remove the need for a reviewer who checks that the change actually does what it claims.

At SWARECO, that's the same bar we hold AI-generated code to in review: the diff still needs a clear description, passing tests, and a human who signs off on the logic, not just the style.

FAQs

1. What is a PR in software development?

A PR, or pull request, is what a developer opens after they create a branch or fork in a distributed version control system. Opening a PR proposes a feature or bug fix and starts the code review process before the change is integrated. Teams working on open-source software use this flow constantly.

2. How do teams create a PR and what should it include?

Developers start by creating a branch or fork, then follow their platform's steps for creating a pull request. The PR should link the originating ticket, add a detailed description, and stay small and focused so reviewing it goes fast.

3. How has artificial intelligence changed PRs?

AI tools now assist with reviewing code, running checks that can catch a likely bug before a human ever looks at the diff. AI can flag issues and suggest fixes, but a person still has to confirm the result before it ships, to avoid a false or unwanted change reaching production.

4. How do PRs fit into DevOps and continuous workflows?

Pull requests enforce a review gate that keeps code integration safe inside a CI system. In a DevOps workflow, changes only merge after automated tests and a manual review both approve them.

5. What best practices help when using AI tools with PRs?

Keep PRs small and focused, write a detailed description, and link the originating ticket so reviewers understand the intent. Keep reviewing the code by hand, run the test suite, and treat AI suggestions as one more input — not the final authority on whether a change is safe to merge.