When to Use Git Commits vs Branches as a Solo AI Developer
A practical decision framework for fast solo workflows with AI-generated code
I was knee-deep in refactoring my authentication system last month when Claude generated a massive chunk of code that touched twelve different files. My first instinct was to commit it all directly to main like I usually do. Then I realized - what if this AI-generated code breaks something? What if I need to roll back just part of it?
This got me thinking about when solo developers like us actually need Git branches, especially when working with AI tools that can generate substantial code changes in seconds. After working through several AI-assisted projects, I've developed a simple framework that keeps the workflow fast while avoiding the headaches.
The Solo Developer's Branching Reality
When you're working alone, most traditional Git advice doesn't apply. You don't need pull requests, code reviews, or complex branching strategies. But AI coding introduces new challenges:
- AI generates large bursts of code quickly
- Code spans multiple files and often needs iteration
- You want to experiment without polluting your main branch
- Sometimes you need to work on multiple AI-generated features simultaneously
The key insight is treating commits and branches as different tools for different situations.
The Decision Framework
Here's the mental model I use:
Commits = Checkpoints - Safe save-points you can rewind to Branches = Sandboxes - Parallel universes you can merge back later
This distinction changes everything about when to use each approach.
When to Just Commit (Default Approach)
For most AI-assisted changes, committing directly to main works perfectly:
Small, self-contained AI generations When you ask your AI assistant to "add error handling to the login function" and it gives you a focused change, just commit it. The scope is clear and the risk is low.
Single-session work If you can complete and test the entire change in one sitting, branches add unnecessary overhead. Ask the AI to generate the code, test it, refine if needed, then commit.
Low-risk experiments VS Code's discard changes feature is your friend here. If you don't like what the AI generated, just discard before committing. No branch needed.
Here's my typical workflow for these scenarios:
# Ask AI to generate feature code
# Test the generated code
# If it works:
git add .
git commit -m "Add user profile validation with AI assistance"
# If it doesn't work:
# Discard changes in VS Code UI and try again
When to Create a Branch
Branches become valuable in specific scenarios that AI coding makes more common:
Multi-day AI-assisted refactors When you're using AI to help restructure large parts of your codebase, a branch keeps the half-finished work isolated. You can still push hotfixes to main while the refactor sits safely in its sandbox.
git checkout -b refactor/ai-auth-system
# Work with AI over multiple sessions
# Commit incremental progress
# Merge when complete and tested
Parallel AI experiments Sometimes you want the AI to try two different approaches simultaneously. Maybe you're exploring both a React and Vue implementation, or testing different API designs.
git checkout -b experiment/vue-dashboard
# Let AI generate Vue version
git checkout main
git checkout -b experiment/react-dashboard
# Let AI generate React version
Uncertain AI-generated features When you're not sure if an AI-generated feature will work out, branches let you experiment without commitment. If the AI's approach doesn't pan out, delete the branch without polluting your history.
Large-scope changes with production concerns This is where branches really shine for solo developers. Imagine you're using AI to migrate from REST to GraphQL across your entire API. It's going to take days, touch dozens of files, and you might discover critical bugs in production during that time.
A branch keeps your experimental work separate while letting you fix urgent issues on main.
Implementing the Framework
The practical implementation is straightforward. I use this simple rule:
Can I complete and verify this AI-generated change in 1-2 coding sessions?
- Yes → Commit to main
- No → Create a branch
Is this experimental or might I abandon it?
- Yes → Create a branch
- No → Commit to main
Do I need to work on other things while this is incomplete?
- Yes → Create a branch
- No → Commit to main
Branch Naming for AI Work
When I do create branches, I use a simple naming convention:
experiment/ai-new-auth-flow # AI-generated experiments
refactor/ai-db-migration # AI-assisted refactoring
feature/ai-payment-integration # AI-generated features
The ai- prefix helps me remember these were AI-assisted, which is useful when reviewing code later.
The Reality Check
Here's what I've learned: most of the time, you don't need branches as a solo AI developer. The overhead usually isn't worth it for the typical "ask AI to add a feature, test it, commit it" workflow.
But when you do need branches - for those big refactors, parallel experiments, or uncertain features - they become invaluable. The key is recognizing those situations and not defaulting to branches for everything.
Conclusion
The combination of solo development and AI coding tools creates a unique workflow that doesn't fit traditional Git branching advice. By treating commits as checkpoints and branches as sandboxes, you can keep most of your work in the fast, simple commit-to-main flow while using branches strategically for complex or experimental AI-generated code.
The framework is simple: default to commits, branch when you need isolation or parallelism. This keeps your workflow efficient while giving you safety nets when AI generates large, uncertain, or long-running changes.
Let me know in the comments if you have questions, and subscribe for more practical development guides.
Thanks, Matija