Using AI to Write Better Commit Messages and PR Summaries

An AI git copilot is a tool that uses artificial intelligence to help developers write clearer, more consistent commit messages and pull request (PR) summaries. By analyzing the changes made to the codebase (the “diff”), these tools automatically generate descriptive text that explains what was changed and, in some cases, why it was changed. This helps streamline the development workflow, improve team collaboration, and make the project history easier to understand.

At its core, an AI git copilot functions by processing the git diff output—the lines of code that have been added, removed, or modified. Here’s a simplified breakdown of the process:

1. Analyzes code changes The AI model scans the diff to identify the key actions taken. For example, it can recognize if a function was renamed, a new variable was introduced, logic was refactored, or a dependency was updated.
2. Understands context Sophisticated models don’t just look at the code in isolation. They consider the surrounding code to understand the programming language, syntax, and common patterns. This allows the AI to generate more relevant and human-like descriptions.
3. Generates a summary Based on its analysis, the AI drafts a commit message or PR summary. Many tools can be configured to follow specific formats, such as the widely-used Conventional Commits specification, which prefixes messages with types like feat:, fix:, or chore:.

For pull requests, the AI can synthesize information from multiple commits into a single, high-level summary, making it easier for reviewers to grasp the overall impact of the proposed changes.

Automating commit and PR descriptions isn’t just about saving a few minutes. It’s about improving the quality and clarity of a project’s history. Here are some common use cases:

• Solo developers For individuals working on personal projects, maintaining a clean git history is crucial for tracking progress and understanding past decisions. AI-powered commit messages ensure that even small, frequent commits are well-documented.
• Small to mid-sized teams In a collaborative environment, clear communication is key. AI-generated PR summaries give reviewers immediate context, reducing the time it takes to understand a change and provide feedback. This accelerates the review cycle and helps teams move faster.
• Large enterprises In large organizations with many contributors, maintaining a standardized and coherent commit history is a significant challenge. AI tools can enforce formatting conventions automatically, ensuring that the codebase’s history remains a valuable asset for years to come.
• Open-source projects Open-source maintainers often spend a significant amount of time cleaning up contributors’ commit messages and PR descriptions. An AI copilot can lower the barrier to entry for new contributors by helping them submit well-formed pull requests from the start.

While AI can be a powerful assistant, it’s not a replacement for developer judgment. It’s important to be aware of the limitations:

• AI doesn’t know the why A key principle of a good commit message is to explain the intent behind a change. An AI can see what changed, but it can’t read your mind to understand the business reason, the user feedback that prompted the change, or the alternative solutions you considered.
• The risk of generic messages If used without care, AI can produce generic or overly simplistic messages that don’t add much value. For example, a message like fix: updated function is not much better than a lazy, human-written one.
• Security and privacy When using a cloud-based AI tool, your code diffs are sent to a third-party server for processing. While reputable providers have strong privacy policies, it’s a factor to consider, especially when working on proprietary or sensitive codebases.

The most effective workflow is one where the AI provides a solid first draft, and the developer refines it with their unique context and intent.

To get the most out of these tools, it’s important to integrate them thoughtfully into your workflow.

• Always review and edit Treat the AI’s output as a starting point, not the final version. Read it over, correct any inaccuracies, and add the crucial context that only you can provide.
• Configure it to your standards Most tools allow you to set a preferred format. Take the time to configure it to follow your team’s conventions, whether that’s Conventional Commits, a specific tense (e.g., imperative mood like “Add feature” vs. past tense “Added feature”), or a custom template.
• Combine with small, atomic commits AI works best when it has a focused set of changes to analyze. By making small, atomic commits that each address a single concern, you’ll get more accurate and meaningful suggestions from the AI.
• Use it to learn Pay attention to the messages the AI generates. You may discover more precise ways to describe your changes or learn about conventions like Conventional Commits, ultimately making you a better communicator.

While Kinde’s focus is on authentication, authorization, and user management, adopting modern development practices like using an AI git copilot aligns with the goal of building better software faster. A clean, well-documented git history makes it easier to maintain and secure your application over the long term.

Just as Kinde abstracts away the complexity of authentication, AI git copilots abstract away the tedious parts of documenting your work, freeing you up to focus on what matters most: building great products for your users.

• Kinde quick start guides