I've been skeptical about AI-generated commit messages. Most of the time they're generic garbage like "updated file" or "fix bug." But I just forced myself to use Copilot's commit message suggestion feature properly, and it's not terrible—if you follow a specific workflow.
The key is staging your changes first. Don't just open the commit message box with unstaged changes. Stage the specific changes for the commit you want to describe. *Then* open the command palette and run "Copilot: Generate Commit Message." The context of the staged diff seems to be what makes the difference.
Example: I had staged a change that added input validation to an API endpoint. Copilot suggested:
```
Add validation for customer_id parameter in get_customer_details endpoint
- Check for integer type and positive value
- Return 400 Bad Request with error details on invalid input
```
That's actually usable. It's specific and describes *what* and *why*. I've found it works best with smaller, logical commits. If you stage 20 files with massive refactors, it still falls apart.
A few observations:
* It's pulling from patterns in your own repo's history and the actual code diff.
* The suggestions are hit-or-miss for complex architectural changes, but solid for discrete feature adds or bug fixes.
* You still need to review and edit, but it's a 70% complete draft, which is a time-saver.
Has anyone else benchmarked this against writing their own? I'm curious about the time saved over, say, 100 commits.
Show me the query.
Oh, the staging first tip is key. I've been using it wrong too, just asking for a message on a mess of unstaged files. No wonder it gave bad results.
Do you find it works better for certain types of changes? Like, maybe bug fixes vs. new features?
That's a good observation about different change types. Based on my own testing, I've found the quality does seem to vary depending on the context and clarity of the diff.
It tends to perform better with well-contained, single-purpose changes. For a bug fix where the diff clearly shows a conditional being added or a variable being corrected, it can accurately summarize the "what" and sometimes the "why." New feature additions are hit or miss; if the staged code is a new, self-contained module, the message is often decent. However, if the feature spans multiple architectural layers and you've only staged one file, the suggestion becomes myopic and misses the broader intent.
The real weakness I've seen is with refactoring. Staging a batch of renamed variables or a extracted method usually yields a painfully generic message that captures the literal code change but completely misses the structural improvement, which is the entire point of the commit.
RTFM — then ask for the audit
Yes, staging first is the secret sauce. I've been using this exact workflow for a few months, and it consistently works.
One caveat I've noticed: it gets much better if you have a clean, descriptive commit history in that repo already. It seems to pick up on your team's phrasing patterns. So if your past messages are messy, the suggestions might stay a bit generic.
I also find it struggles with config changes or dependency updates unless the diff is very clear. For those, I usually just write my own. But for actual feature work, it's a real time-saver.
Automate everything.