You inherited a project. Or maybe you built it yourself back when “just push to main” felt like enough process. Either way, you’re staring at a repo with dozens, hundreds, or even thousands of commits, no version tags, and no changelog. The git log is your only record of what changed and when.
Manually sifting through commit history to reconstruct versions sounds tedious. It is. But this is exactly the kind of task AI handles well.
Why Bother
A clean release history isn’t just aesthetic. It’s functional.
- Rollbacks become trivial.
git checkout v1.2.0beats scrolling through commits trying to remember which one was stable. - Changelogs help clients. When someone asks “what changed in the last update?” you have an answer that isn’t “let me check git log.”
- Semantic versioning communicates intent. A major version bump tells people to expect breaking changes. A patch says it’s safe to update.
- CI/CD can hook into tags. Automated deployments, release notes, and notifications all work better with proper versioning.
Let AI Do the Archaeology
Instead of manually reading through every commit, hand the task to an AI assistant that has access to your codebase. Tools like Claude Code can analyze your git history, identify logical release points, and generate the tags and changelog for you.
Here’s the prompt I use:
Analyze the git history of this repository and backfill a proper release history. Identify meaningful release points, assign semantic version numbers, create annotated tags, and generate a CHANGELOG.md following the Keep a Changelog format. Start with v1.0.0 for the first production-ready commit. Use your judgment to group related commits into logical releases.
That’s it. The AI knows how to read git history, understands semantic versioning, and can format a changelog correctly. You don’t need to specify which commands to run or spell out what MAJOR/MINOR/PATCH mean. It figures that out.
What You Get
After running this workflow, you’ll have:
- Tagged commits you can navigate with
git checkout v1.2.0 - A changelog that answers “what changed?” without digging through commits
- A foundation for proper release management going forward
The AI handles the tedious parts: reading commit messages, categorizing changes, formatting the changelog correctly. You review the output and adjust if needed.
When Commits Are Vague
Some repos have commit history that looks like:
abc1234 updates
def5678 more updates
ghi9012 fixes
jkl3456 final fixes
AI can still work with this. Just mention the problem:
The commit messages in this repo aren’t descriptive. Base the changelog on actual code changes, not commit messages.
It’ll look at diffs instead of relying on messages. The changelog entries will be broader, but that’s fine. You’re creating a usable history, not a perfect one.
Maintaining It Going Forward
Once you’ve backfilled, keep the pattern going. After completing work, a simple prompt handles the release:
Create a new release. Look at the changes since the last tag, determine the appropriate version bump, update the changelog, commit, tag, and push.
This takes seconds and keeps your release history clean without manual bookkeeping.
The Time Investment
Backfilling a typical project takes 10-15 minutes of AI processing time. You spend maybe 5 minutes reviewing the output. Compare that to hours of manually reading commits and writing changelog entries.
The best time to start versioning was at project inception. The second best time is now, and AI makes “now” a lot less painful.