AI Commit Message Generator: A Git History You Can Read

An AI commit message generator reads your staged changes and drafts a clear summary of what changed and why, so your Git history stays readable without slowing you down. It works best for the “what” part of a commit. The “why”, the reason behind the change, still has to come from you, because it usually lives in your head, a ticket or a conversation the AI never saw.

This guide explains what makes a good commit message, how to use AI to write one from a diff, which conventions to follow, the mistakes AI makes with commits, and a simple workflow you can use every day.

Why commit messages matter

A commit message is a note to the next person who reads the code, very often yourself in six months. When something breaks, git log, git blame and git bisect are how you find out what changed and why. A history full of “fix”, “wip” and “changes” makes that work slow and frustrating.

Good messages pay off in specific moments:

• Debugging. Finding the commit that introduced a bug, and understanding whether the change was intentional.
• Code review. Reviewers understand the intent before reading the diff.
• Release notes. Structured messages can be turned into a changelog almost automatically.
• Onboarding. New colleagues learn the codebase’s history from the log.

The reason so many histories are poor is simple: writing a good message takes a minute when you just want to move on. That minute is exactly what an AI commit message generator can save.

What a good commit message looks like

The widely followed conventions, summarised well in the article How to Write a Git Commit Message, come down to a few rules.

• A short subject line, about 50 characters, ideally no more than 72.
• Imperative mood: “Add retry to payment webhook”, not “Added” or “Adds”.
• No full stop at the end of the subject.
• A blank line, then a body wrapped at around 72 characters.
• The body explains what and why, not how. The diff already shows how.
• References to tickets or issues where your team uses them.

An example

Weak: fix stuff in checkout

Better:

Retry payment webhook on provider timeout

The payment provider occasionally answers after 10 seconds, which our
handler treated as a failure and left orders unpaid. Retry up to three
times with backoff before marking the payment as failed.

Refs #482

The second version tells a future reader what happened, why it was a problem and what the fix does, without them opening the code.

Conventional Commits in brief

Many teams use the Conventional Commits specification, which adds a type prefix to the subject line. That makes the history machine-readable: tools can generate changelogs and decide version numbers from it.

Type	Meaning	Example subject
feat	A new feature	feat(cart): add saved-for-later list
fix	A bug fix	fix(auth): handle expired refresh tokens
docs	Documentation only	docs: explain local SSL setup
refactor	Code change with no behaviour change	refactor(api): extract pagination helper
test	Adding or fixing tests	test(invoice): cover zero-VAT case
chore	Maintenance, tooling, dependencies	chore: bump image library to latest patch
perf	Performance improvement	perf(search): cache category counts

A breaking change is marked with an exclamation mark after the type, such as feat(api)!:, or a BREAKING CHANGE: footer. If your team uses this format, tell the AI explicitly; it is one of the things models follow well when asked and drift from when not.

How to use an AI commit message generator

1. Give it the staged diff

The AI needs to see what changed. Run git diff --staged and paste the output, or attach it as a file. For large changes, git diff --staged --stat plus the most important hunks is often enough. Only include what is actually staged, or the message will describe changes that are not in the commit.

2. Add the why in one sentence

This is the step that turns a generic message into a useful one. Write a line such as “Customers in Latvia saw prices without VAT on the product page” or “Ticket 482: orders stay unpaid when the provider is slow”. The AI can describe the code; it cannot know the reason.

3. State your format

For example: “Write a commit message in Conventional Commits format. Subject in imperative mood, under 60 characters. Then a blank line and a body of 2 to 4 lines explaining what changed and why, wrapped at 72 characters. Add ‘Refs #482’ at the end.”

4. Review and edit

Read the message against the diff. Check that it does not claim more than the change does, that the type is right and that the subject would make sense to someone scanning a long log.

5. Commit

Paste the message into your editor with git commit, or use git commit -F message.txt if you saved it to a file. The git commit documentation lists all the options.

Setting it up for a team

An AI commit message generator helps most when everyone on the team uses the same rules. Otherwise you swap one inconsistent history for another.

Write the convention down once

Put your format on one page: which types you use, whether scopes are required, the subject length limit, how to reference tickets and what counts as a breaking change. That page becomes both the team guideline and the instruction you give the AI.

Use a reusable prompt

A shared prompt keeps results consistent. For example:

• “You write Git commit messages for our team.”
• “Format: Conventional Commits. Allowed types: feat, fix, docs, refactor, test, chore, perf. Scope is the top-level folder name.”
• “Subject: imperative mood, under 60 characters, no full stop.”
• “Body: 2 to 4 lines, wrapped at 72 characters, explaining what changed and why. Use only the reason I give; never invent one.”
• “Footer: ‘Refs #NUMBER’ if I give a ticket number.”

Check it automatically

A commit-message hook or a check in your CI pipeline can reject messages that break the format, whoever or whatever wrote them. That catches the occasional AI draft that drifts, and the occasional human one too.

Keep humans responsible

Make it explicit that the person committing owns the message. AI drafts it; the developer approves it. That keeps the history honest when someone later relies on it during an incident.

Mistakes AI makes with commits

AI drafts are usually fluent and usually close. These are the errors to look for.

• Describing the how instead of the why. “Change timeout from 10 to 30 and add loop” is a summary of the diff, not a reason.
• Inventing motivation. Without your one-line reason, the model may guess one, such as “to improve performance”, that is simply untrue.
• Over-claiming. “Fix all checkout issues” when the commit fixes one.
• Wrong type. Calling a refactor a fix, or a behaviour change a refactor. This matters if your versioning depends on types.
• Too long a subject. Long subjects get truncated in many Git tools.
• Bundling. If the AI needs three paragraphs to describe the commit, it may really be three commits.

The last point is a useful side effect: if the generated message reads like a list of unrelated changes, split the commit with git add -p before committing.

Beyond single commits

Pull request descriptions

The same approach works for pull requests. Give the AI the commit list and the main diff, plus the reason for the work, and ask for a summary, a testing checklist and any risks. Our guide to code review with AI covers how to review the changes themselves.

Changelogs and release notes

With a structured history, you can paste git log --oneline between two tags and ask for user-facing release notes grouped by features, fixes and breaking changes. Ask the AI to leave out internal chores and to write for users, not developers.

Cleaning up history before a merge

If your branch has ten “wip” commits, an assistant can suggest how to squash them into two or three meaningful ones and draft the messages. Be careful with history that others have already pulled.

Understanding old history

Working in reverse, an assistant can explain what a cryptic old commit did from its diff. That pairs well with our guide on understanding an unfamiliar codebase with AI.

Privacy and what not to paste

Diffs can contain secrets: API keys in configuration files, connection strings, customer data in test fixtures. Before pasting a diff into any AI tool, check it.

• Never commit secrets in the first place; use environment variables or a secret manager.
• If a diff contains a key, remove it before pasting, and rotate the key, because it is now in your Git history.
• Follow your company’s rules on sharing proprietary code with external services.

Writing commit messages with Ask Mio

Ask Mio’s Code mode, included on the Coding and Business plans, writes code with explanations, fixes bugs from a pasted error and refactors. Mio routes coding requests to coding models, so you do not have to choose one. Drafting a commit message from a pasted diff is a small, quick job, typically a coding answer costing 5 to 10 points.

• Save your format once. Put your commit conventions in a project’s instructions, and every chat in that project follows them.
• GitHub connector. With the GitHub connector, Mio can read your repositories, issues, pull requests and file contents when you ask, which helps when the reason for a change is in an issue. See our guide to developer and project connectors.
• Privacy. Chats and files are not used to train models, and Ask Mio runs on servers in the EU.

Where another tool is better: if you want the commit message drafted inside your editor or Git client with one click, an AI feature built into your IDE will be more convenient for this one task. Mio is the better fit when you want the same assistant for commits, code review, documentation and everything else.

Frequently Asked Questions

Can AI write good commit messages?

Yes, for describing what changed. Given a staged diff, an AI produces a clear subject line and summary quickly and follows formats such as Conventional Commits well. It cannot know why you made the change unless you tell it, so add a one-sentence reason to every request. Then review the message against the diff before committing.

How do I give the AI my changes?

Run git diff --staged and paste the output, or save it to a file and attach it. For large changes, include git diff --staged --stat plus the most important parts of the diff. Make sure only the staged changes are included, and remove any secrets such as API keys or passwords before you paste anything into an AI tool.

Should I use Conventional Commits?

If you want automatic changelogs or version numbers from your history, yes. The type prefixes such as feat, fix and refactor make the log machine-readable and easier to scan. For small personal projects, a clear imperative subject line and a short “why” body may be enough. Either way, tell the AI which format you use so it stays consistent.

What should a commit message body contain?

The body should explain what changed and why, in a few lines wrapped at about 72 characters. Leave out how the code works, because the diff shows that. Mention the problem the change solves, any side effects or limitations, and a reference to a ticket or issue if your team uses them. If the body becomes very long, consider splitting the commit.

Is it safe to paste code diffs into an AI tool?

It depends on what the diff contains and on your company’s rules. Remove secrets and personal data first, and follow any policy about sharing proprietary code with external services. Choose a tool that does not train models on your data and states where it stores it. If a key slipped into a diff, rotate it regardless.

The Bottom Line

An AI commit message generator removes the small friction that makes Git histories messy. Give it the staged diff, one sentence on why, and your format, then check the draft against the change before committing. You get readable history, better reviews and easier release notes for a few seconds per commit. To try it with Code mode, a project for your conventions and the GitHub connector, see Ask Mio’s plans, or start on the free plan to test the writing side first.