Part 3: GitFlow + AI Development Tool-Based Collaboration Practice

2 Jan 2026 4 min

Intermediate ★ ★ ★ ☆ ☆

Created at 2 Jan 2026

Course Overview

This course covers Git collaboration workflows needed when transitioning from individual to team projects.

We start not by listing GitFlow rules, but by understanding why such a structure is necessary.

In particular, we emphasize that in an era where AI coding tools have become ubiquitous, Git goes beyond simple version control to serve as a “safety net for AI-generated code.”

Learning Objectives

Fundamentally understand why branch strategies are necessary
Grasp each GitFlow branch’s role and flow
Learn how Pull Requests and code reviews actually work
Learn how to leverage AI tools for commit messages and review assistance
Internalize the development cycle connecting AI + testing + GitFlow

Course Structure

Part 1: Why Branch Strategies Are Necessary

Problems created by “pushing directly to main”
Differences between solo development and team development
Branches = work isolation + merge control
Real conflict cases from actual team projects

Part 2: Understanding GitFlow as a Mental Model

Roles of main, develop, feature, release, and hotfix
What each branch “protects”
GitFlow vs GitHub Flow vs Trunk-based: When to use what
Simplified GitFlow suitable for labs and undergraduate teams

Part 3: The AI Era Development Cycle — AI + Testing + Git

In an era where AI coding tools generate code, Git is not just a version control tool. It serves as a “checkpoint” that allows you to safely revert when AI-generated code doesn’t work as expected.

flowchart TD A[Create feature branch

git checkout -b feature/new-function] B[Request code from AI

'Write a function to validate user input'] A --> B C[Run tests

pytest tests/] B --> C C --> D{Test results} E[commit

Proceed to next task] D -->|Pass| E F[git checkout -- .

Re-request from AI with error message] D -->|Fail| F F --> B

Why This Cycle Matters

AI-generated code often “looks plausible but is wrong.” Accumulating AI code without testing makes it difficult to trace where problems started. Combining Git’s version control with testing enables:

Failure isolation: Revert only the failing changes when tests fail
Safe experimentation: Try AI code freely on branches, discard if it doesn’t work
Incremental integration: Merge only validated code in small units to develop

Practice: Experience the AI + Testing + Git Cycle

Request function implementation from AI
Validate with pre-written tests
On failure, revert with git checkout and retry
On success, record with meaningful commit message

Part 4: Pull Requests and Code Review

Why PRs are more than just merge requests
Components of a good PR: title, description, scope of changes
What to look for and what not to look for in code review
Communication tips for both reviewers and authors
Special considerations when reviewing AI-generated code

Part 5: AI-Based Collaboration Tools

Auto-generating commit messages with AI
Using AI to draft PR descriptions
Introduction to AI code review assistance tools
Limitations of AI tools and the role of humans

Part 6: Real Team Project Simulation

Conduct a virtual project in teams of 3-4
Create feature branch → Write code with AI → Test → PR → Review → Merge
Practice resolving conflicts
Retrospective: What went well and what was difficult

Course Format

Online/Offline: Zoom or in-person sessions
Hands-on focused: Team collaboration simulation in actual GitHub repositories

Target Audience

Undergraduates starting their first team project
Teams preparing graduation projects or capstone projects
New lab members working with shared codebases
Developers who have only used Git solo without collaboration experience

Prerequisites

Basic Git commands (add, commit, push, pull)
GitHub account and basic usage experience
Ability to write simple code in Python or another language
Basic pytest usage (TDD course completion recommended)

Key Practice Examples

Creating feature branches and working on them
AI-generated code → test → commit/rollback cycle experience
Writing PRs and exchanging reviews
Resolving merge conflicts
Generating commit messages with AI tools

AI Era Development Cycle Summary

Step	Action	Tool
1. Isolate	Create feature branch	Git
2. Generate	Request code from AI	Copilot, Claude, etc.
3. Validate	Run tests	pytest
4a. Success	Commit and next task	Git
4b. Failure	Revert and re-request	Git + AI
5. Integrate	Create PR and review	GitHub
6. Merge	Merge to develop	Git

GitFlow Branch Roles Summary

Branch	Purpose	Created When	Merges To
main	Stable, deployable version	Always exists	-
develop	Next release integration	Always exists	main
feature/*	Individual feature development	When feature starts	develop
release/*	Release preparation and QA	Before release	main, develop
hotfix/*	Emergency bug fixes	When bug found	main, develop

Good Commit Message Structure

1
2
3
4
5
<type>: <subject>

<body>

<footer>

type: feat, fix, docs, style, refactor, test, chore
subject: Under 50 characters, written in imperative mood
body: Explain why this change is needed
footer: Link issue numbers (e.g., Closes #123)

Next Steps After This Course

After completing this course, you can continue with the DevOps / MLOps Minimum Set course, where you’ll learn how to scale team-level collaboration to organization-level stable operations.

Contact

For inquiries about course schedules and pricing, please reach out via email.