AI-generated commit messages: the times they're great and the times they're bad

Published 2026-05-11 by Owner

A good commit message does three things: it summarizes what changed, it explains why the change happened when that isn’t obvious, and it points to the relevant issue or ticket. AI can handle (1) reliably, does a decent job at (3) if you give it context, and often fails at (2) in exactly the cases where (2) matters most.

That’s the shape of the problem. The rest of this guide fills in what that looks like in practice and how to work with it rather than against it.

What makes a commit message good

Before getting into AI, it’s worth being precise about what a commit message is supposed to accomplish. A message that reads:

Update auth middleware

tells a future reader almost nothing. They can see from git diff that auth middleware was updated. The commit message restating that fact adds zero signal.

A message that reads:

fix(auth): reject expired JWTs before reaching route handlers

Tokens were passing through on the first request after a deployment
gap because the middleware was reading cached expiry values.
Fixes #481.

does something different. It states the type of change (fix), the scope (auth), and a one-line summary. The body explains the specific failure condition — something no automated tool can infer from the diff alone. The issue reference closes the loop with the tracker.

That second message is genuinely harder to write. It requires knowing why the bug existed, not just what changed to fix it. That’s the gap AI runs into.

Small diffs vs large diffs

AI-generated commit messages are most reliable on small, focused diffs. A three-line rename across a single file? The model sees the entire before-and-after and produces an accurate summary. A formatting pass? Same thing — the diff is mechanical and the message should be too.

The failure mode appears on large diffs. When a diff spans dozens of files and hundreds of lines, the model summarizes aggressively. The output tends to be:

refactor: update multiple files for consistency

That’s not wrong, but it’s useless. A reader needs to know which consistency issue was addressed and why it mattered. “Update multiple files for consistency” is the commit message equivalent of writing a function named doStuff.

Large diffs also tend to represent the changes where the context is most important. Big refactors, architectural shifts, multi-file bug fixes — these are exactly the changes where future readers most need the “why.” The AI-generated message gives them the least.

There’s a useful heuristic for gauging when AI will struggle: if you’d need to read the PR description to understand a commit, the AI definitely can’t produce a good message from the diff alone. The diff shows what lines changed; the PR description holds the decision-making that led there. That decision-making is what belongs in the commit body, and it can only come from you.

The practical upshot: use AI for small, bounded changes. For anything that requires reading multiple files to understand what’s going on, draft the message yourself or start with AI and add significant context.

Conventional commits and AI

The Conventional Commits format turns out to pair well with AI because the structure is rigid enough that the model can fill it in reliably:

<type>(<scope>): <short summary>

<body>

<footer>

Types like feat, fix, refactor, chore, docs, and test have clear definitions. Scope is usually the module or subsystem name. The model handles these classifications accurately most of the time — it can read a diff and determine whether something is a bug fix versus a feature addition.

The part where Conventional Commits helps is the subject line. With a required format, the model doesn’t invent a vague subject — it has to pick a type and write a scope-specific summary. That constraint produces more useful output than an open-ended prompt.

Here’s a prompt pattern that works reliably:

Generate a Conventional Commits message for this diff.
Format: <type>(<scope>): <short summary under 72 chars>

Optional body: 1-3 sentences explaining why if not obvious.

Diff:
<paste diff>

The “if not obvious” qualifier on the body matters. Without it, the model writes a body that just restates the subject. With it, it at least attempts to reason about motivation — and even when that reasoning is wrong, it gives you something to correct rather than nothing.

The “summary too vague” smell

There’s a specific output pattern to reject immediately. It looks like this:

Update X

Improve Y handling

Fix issue with Z

These messages fail the basic test: a reader looking at this commit six months from now gets no additional information beyond “something happened to X/Y/Z.” They still have to read the diff to understand anything. The commit message has failed its job.

When AI produces these, the right move is to reject and re-prompt with more context. Useful re-prompt additions:

The bug or issue the change addresses: “This fixes a race condition in the session cache where…”
The approach taken when multiple approaches were possible: “We chose to debounce here instead of throttle because…”
What was tried and didn’t work, if applicable

If you can’t provide that context because you don’t know the answer, that’s useful information too — it means the change might not be fully understood and worth thinking through before committing.

Manual review is not optional

The right workflow is: AI drafts, human reads and edits, human commits.

That middle step is not a formality. The draft is a starting point, not a finished product. Three things to check before accepting:

Is the summary accurate? The model can misread diffs, especially when the diff involves complex type changes or logic that spans multiple files. A commit message that describes the wrong change is worse than a vague one — it actively misleads future readers.

Does the body explain the why? If the body is absent or it just restates the subject line, add the motivation yourself. Two sentences is usually enough: what the problem was, and why this approach addresses it.

Does the subject line pass the vagueness test? Read it aloud and ask whether a colleague could understand what changed and why from that line alone. “Update auth code” fails. “fix(auth): reject expired JWTs before middleware chain” passes.

This review step takes about thirty seconds for a typical change. It’s faster than writing the message from scratch and catches the cases where the AI output would mislead rather than inform.

A fourth thing worth checking on any change that touches security, performance, or data integrity: whether the message should carry a warning for future readers. “This migration is irreversible” or “disabling this check is intentional — see issue #312” belongs in the commit body. The AI will not add it because it can’t know that context exists. You have to supply it.

What this workflow actually looks like

To make it concrete: here’s a realistic session with Cursor or Copilot doing the initial draft.

The diff is a twelve-line change to a rate limiter that adds an exception for health-check endpoints. The AI produces:

fix(ratelimit): update rate limiting logic

That’s the vague-summary smell. Re-prompt: “The change adds an exemption for /health and /readiness endpoints so they don’t burn through rate-limit buckets during load balancer checks. Please regenerate.”

The model produces:

fix(ratelimit): exempt health-check endpoints from rate limiting

Load balancer health checks at /health and /readiness were consuming
rate-limit quota, causing false positives on high-traffic instances.

That’s usable. Read it, confirm it matches the diff, check the scope is right, and commit.

The re-prompt added twenty seconds. The commit message now tells a complete story.

Where this leaves things

AI-generated commit messages are a net positive for most development workflows, with the constraint that they require active review rather than passive acceptance. The structural parts — type, scope, subject line format — are reliably correct. The contextual parts — why a change happened, what problem it solves — require human input in proportion to how non-obvious the change is.

The cases where AI commit messages are fine with minimal review: renames, formatting changes, documentation updates, adding tests for already-understood behavior. The cases where they need significant editing: bug fixes with a non-obvious root cause, refactors that address specific failure modes, architectural changes that reflect a decision that was debated.

One way to calibrate expectations: ask whether a developer six months from now, looking at this commit for the first time, would understand both what changed and why. If the AI-generated message answers only the first half of that question, the message is unfinished. The thirty seconds it takes to add the second half is the entire cost of the review step — and it pays back every time someone reads the history.

Treating AI commit message generation as “AI drafts, you finish” keeps the useful part (you don’t have to type the boilerplate) while catching the failure mode (vague summaries that erode the usefulness of your git history over time).