AI-Powered Git Workflows: Commits, PRs, and Code Reviews

Git is the backbone of every developer’s workflow, but the meta-work around git, writing commit messages, describing PRs, reviewing diffs, is time-consuming and often done poorly when we’re rushing. AI is quietly transforming every part of this workflow, and I’ve been refining my setup over the past year.

Here’s my current AI-powered git toolkit.

AI-Generated Commit Messages

I stopped writing commit messages by hand months ago. Not because I can’t, but because AI does it faster and more consistently. The key is generating messages from the actual diff, not from memory of what you changed.

Here’s a git alias I use:

# In .gitconfig
[alias]
  cm = "!f() { 
    diff=$(git diff --cached); 
    msg=$(echo "$diff" | llm -s 'Write a conventional commit message for this diff. Use format: type(scope): description. Keep under 72 chars. Types: feat, fix, refactor, docs, test, chore. No body unless the change is complex.'); 
    git commit -m "$msg"; 
  }; f"

Stage your files, run git cm, and you get a properly formatted commit message derived from the actual changes. No more “fix stuff” or “update code” commits.

The generated messages look like:

feat(auth): add refresh token rotation on login
fix(cart): resolve NaN total when quantity is empty string
refactor(api): extract shared error handling into middleware
test(utils): add edge case coverage for date formatting

Semantic Commits with AI

Beyond just generating messages, AI enforces semantic commit conventions without you having to think about it. It reads the diff and determines whether it’s a feature, fix, refactor, or chore. This matters because semantic commits enable automated changelogs, semantic versioning, and better git history readability.

I’ve found that AI correctly categorizes commits about 95% of the time. The 5% where it’s wrong are usually ambiguous cases where even humans would disagree, like a refactor that also fixes a subtle bug.

AI-Written PR Descriptions

This is the biggest time saver in my workflow. A good PR description includes a summary of changes, the motivation, testing notes, and any deployment considerations. Writing that by hand for every PR takes 10-15 minutes. AI does it in seconds.

My approach: generate the PR description from the full diff against the base branch.

git diff main...HEAD | llm -s '
Write a pull request description for these changes.
Format:
## Summary
[2-3 bullet points of what changed]

## Motivation
[Why these changes were needed]

## Changes
[Detailed list of modifications]

## Testing
[How to verify these changes work]

## Notes for Reviewers
[Anything the reviewer should pay attention to]
'

The output is thorough and accurate because it’s derived from the actual code changes, not from your memory of what you did three hours ago. I review it, tweak the motivation section to add context the diff can’t convey, and submit.

Automated Changelog Generation

If you’re using semantic commits (and you should be), AI can generate changelogs from your git history:

git log v1.2.0..HEAD --oneline | llm -s '
Generate a changelog from these commits. Group by:
- Features (feat commits)
- Bug Fixes (fix commits)
- Improvements (refactor commits)
- Other (everything else)
Write each entry as a user-facing description, not a commit message.
'

This turns feat(cart): implement quantity stepper with bounds checking into “Added quantity controls to the shopping cart with minimum and maximum limits.” User-facing language from developer commits, automatically.

Code Review with AI

I use AI as a first-pass reviewer before requesting human review. The prompt:

Review this diff for:
1. Bugs or logic errors
2. Missing error handling
3. Performance concerns
4. Security issues (SQL injection, XSS, etc.)
5. Inconsistencies with common patterns

Don't comment on style or formatting.
Only flag things that could cause real problems.

That last instruction is critical. Without it, AI reviews devolve into nitpicking about variable names and bracket placement. With it, you get actionable feedback about actual issues.

AI code review catches about 30% of the issues a human reviewer would find. That’s not a replacement for human review. But it means the human reviewer can focus on architecture, design, and domain-specific concerns instead of spotting a missing null check.

Pre-Commit Hooks with AI

I run a lightweight AI check as a pre-commit hook that scans staged files for common issues:

#!/bin/sh
# .husky/pre-commit

# Check for accidentally committed secrets
staged=$(git diff --cached --name-only)
for file in $staged; do
  if git show ":$file" | grep -qiE '(api_key|secret|password|token)s*[=:]s*["''']''.+["''']''; then
    echo "WARNING: Possible secret in $file"
    exit 1
  fi
done

For more sophisticated analysis, pipe the staged diff to an AI model that checks for common anti-patterns specific to your project. This adds a few seconds to each commit but catches issues before they enter the git history.

Practical Git Aliases

Here are the aliases I use daily:

[alias]
  # AI commit message from staged changes
  cm = "!f() { ... }; f"

  # Summarize branch changes vs main
  summary = "!git diff main...HEAD | llm -s 'Summarize these changes in 3-5 bullet points'"

  # Quick branch description
  whatshere = "!git log main..HEAD --oneline | llm -s 'What feature is being built on this branch? One sentence.'"

  # Find related commits
  related = "!f() { git log --all --oneline | llm -s "Which of these commits are related to: $1"; }; f"

These aren’t gimmicks. They’re tools I reach for multiple times a day. The summary alias is particularly useful when context-switching between branches. Instead of reading through commits, I get a quick summary and I’m up to speed.

AI isn’t going to learn your codebase’s history or understand your team’s conventions deeply. But for the mechanical parts of git workflow, it’s already indispensable.