You have spent six steps learning to read GitHub. This step is about what happens when AI tools start writing to it on your behalf.
When the AI commits code, you need to know what that means
Tools like Cursor, Lovable, and Claude Code can, depending on how they are configured and connected, generate code and push it directly to a GitHub repository. That means they can create files, open pull requests, and add commits, all without you writing a single line yourself. This is genuinely useful. It is also exactly the situation where GitHub literacy pays off.
When an AI tool pushes code, the same signals you have been reading throughout this guide apply. The commits appear in the commit history. The changes appear in pull requests. The files land in the repository. None of that changes. What changes is that the author is a machine working from your instructions, so the quality of what ends up in the repository depends heavily on how clearly you directed it.
What to check after an AI tool makes changes
Start with the pull request, if the tool created one. Read the description. A well configured AI tool will summarise what it changed and why. If the description is vague or missing, look more carefully at the files.
Go to the Files changed tab. You do not need to understand every line of code. Look at the scope. Did the AI touch two files or twenty? A small, focused change is easier to verify and easier to reverse. A change that touches dozens of files across the whole repository deserves more scrutiny before anyone merges it.
Check the commit messages. Same rule as reading any project: specific messages like fix form validation on checkout page tell you something. Generic messages like update files tell you nothing, and they make it harder to trace problems later. If you are setting up an AI tool to commit on your behalf, configure it to write clear commit messages. Most tools let you do this.
You are still the reviewer
AI tools do not know your business. They know how to write code that looks correct. Those are different things. A tool might generate a checkout flow that works technically but skips a compliance step your industry requires. It might rename something in a way that makes sense to the model but breaks a convention your team relies on.
The pull request review is your moment to catch this. You do not need to read the code line by line. Ask yourself: does the description match what you asked for? Does the scope look right? Are there files being changed that should not be? If something looks off, leave a comment. That is what the comment thread is for.
If you are working with a developer who will also review the pull request, your job is not to duplicate their technical review. Your job is to verify that the change does what the brief asked for, and flag anything that contradicts the product, the policy, or the user need. That is the kind of review a developer often cannot do alone.
GitHub as the shared record
One underrated benefit of using AI tools that work through GitHub: you get a paper trail. Every change the tool makes is recorded. Every file that was touched, every version that was merged, all of it sits in the repository history. Some tools also write the prompt context into the commit message or pull request description, which makes the trail even more useful. Whether that happens depends on the tool and how it is set up, but it is worth checking.
This matters when something breaks. Instead of asking "what did we change last week?", you can open the commit log and see exactly what changed, when, and in which file. You learned to read that in step six. Now you have a reason to use it regularly.
It also matters when you hand work to another person. A developer joining a project can look at the pull request history and understand what the AI built and how. A clean, documented repository is a better handoff than a folder of files with no context.
The habit that keeps you in control
Code that looks correct is not the same as correct code. Before anything gets merged, run four checks. Does the pull request description match what you asked for? Does the scope in the Files changed tab look proportionate? Are the commit messages specific enough to be useful when something breaks later? And does anything contradict the product, the policy, or the user need? That last one is a call only you can make.
The paper trail the repository keeps is only useful if it is clean enough to read. Every one of these checks is what makes it readable.