What is AI Changelog — and when to use it
Plain terms: what it does, the problem it removes, and the three signs you need it.
Turn commits and pull requests into release notes people can actually read.
AI Changelog turns recent commits and pull-request notes into human-readable release notes. It groups notable changes by type and orders them latest-first, following the structure of the Keep a Changelog convention, so you stop formatting CHANGELOG.md by hand.
A raw commit log is a build artifact, not a changelog: it is noisy, ungrouped, and full of internal churn. AI Changelog curates the notable changes and groups them so a reader can see what actually shipped and what it means for them.
The output structure follows Keep a Changelog — latest first, grouped change types, version labels. Semantic Versioning still decides what major, minor, and patch mean for your public API; the tool drafts the notes, it does not invent your versioning contract.
Three inputs: your recent commits or notes, the version label you are shipping, and the audience (for example developers, or end users). The audience changes the wording, not the facts.
No. It drafts the notes text. You review, edit, and publish it wherever you keep releases. Keeping a human in the last step is deliberate — release notes are a public commitment.
It summarizes only what you paste. If commit messages are vague, the notes will be vague. It does not verify that a change is actually complete, and it is not a substitute for release testing.
Plain terms: what it does, the problem it removes, and the three signs you need it.
A first pass end to end: collect commits, pick a version, review the grouped draft.
Grouping everything as 'Fixed', shipping raw commit spam, and forgetting the audience.
Where the draft saves time, what still needs a human, and how the two fit together.