Case Study 1: Git Workflow for a Vibe Coding Team

Overview

Company: StreamLine Analytics, a data analytics startup Team size: 5 developers (2 senior, 2 mid-level, 1 junior) Product: A web-based dashboard for real-time business analytics Stack: Python (FastAPI) backend, React frontend, PostgreSQL database AI Tools: Claude Code, GitHub Copilot, Cursor Duration: 3-month workflow redesign and adoption

The Problem

StreamLine Analytics had grown from a one-person prototype to a five-person team in under a year. During the solo phase, the founder (Maya, a senior developer) used a simple workflow: commit to main, deploy when ready. As the team grew, this approach began to collapse.

The immediate symptoms were familiar to anyone who has worked on a team without version control discipline:

  • Merge conflicts every day. With five people committing to main and using AI tools to generate large chunks of code, conflicts were constant and painful.
  • Broken production deployments. An average of two deployments per week broke something because untested AI-generated code was committed directly to main.
  • No way to trace bugs. Commit messages like "updates," "fix," and "AI-generated changes" provided no meaningful history. When a bug appeared, the team could not determine which change introduced it.
  • Review bottlenecks. Maya reviewed all code personally, but the volume of AI-generated code overwhelmed her. She estimated that AI tools had tripled the team's code output, but review capacity had not changed.
  • Lost work. Twice in one month, a developer's local changes were overwritten by a force push from another developer who was trying to resolve a conflict.

The situation came to a head when a production deployment caused a two-hour outage because three developers had independently used AI tools to modify the same authentication module, and the merged result had a subtle bug that allowed unauthenticated access to admin endpoints.

Maya decided it was time to formalize the team's Git workflow.

Discovery and Assessment

Maya spent a week analyzing the team's current practices. She ran a series of Git commands to understand the state of the repository:

# Commit frequency analysis
git log --format='%ai %an' --since='3 months ago' | \
  awk '{print $1, $4}' | sort | uniq -c | sort -rn

# Commit message quality audit
git log --oneline --since='3 months ago' | head -50

# Average diff size
git log --shortstat --since='3 months ago' | \
  grep 'files changed' | \
  awk '{ins+=$4; del+=$6; n++} END {print "Avg:", ins/n, "insertions,", del/n, "deletions per commit"}'

# Conflict frequency (from merge commits)
git log --merges --oneline --since='3 months ago' | wc -l

The results were sobering:

Metric Value Industry Benchmark
Average commit size 347 lines added Under 100 lines
Commits with meaningful messages 23% Above 90%
Merge conflicts per week 12 Under 2
Average PR review time No PRs used Under 24 hours
Broken deployments per month 8 Under 1
Commits with tests 15% Above 60%

Maya also surveyed the team about their AI tool usage:

  • Raj (senior): Used Claude Code for backend development. Generated entire API modules in single sessions. Committed large diffs infrequently.
  • Priya (mid-level): Used GitHub Copilot for frontend work. Committed frequently but with poor messages.
  • Alex (mid-level): Used Cursor for full-stack work. Often worked on the same files as others.
  • Jordan (junior): Used Copilot for everything. Committed everything the AI generated without review.

Designing the Workflow

Maya designed a workflow tailored to the team's size, AI usage patterns, and deployment needs. She made deliberate choices at each level.

Branching Strategy: Feature Branches with Trunk-Based Principles

Maya chose a hybrid approach:

  • main branch is always deployable. Direct commits are forbidden.
  • Feature branches follow the naming convention <type>/<ticket>-<description>:
  • feature/DA-123-user-dashboard
  • bugfix/DA-145-fix-auth-redirect
  • experiment/DA-160-try-graphql-api
  • Branches live for a maximum of 3 days. If a feature takes longer, it is broken into smaller pieces.
  • Daily rebase from main is mandatory to keep branches current.

The experiment/ prefix was critical. It signaled that the branch contained exploratory AI-generated code that might not be merged. This gave developers permission to experiment freely without worrying about code quality on experiment branches.

Commit Conventions: Conventional Commits with AI Disclosure

The team adopted Conventional Commits with one addition: an AI-assisted tag in the commit body when AI tools generated more than 50% of the code.

feat(dashboard): add real-time metrics chart

Add a Chart.js-based line chart that displays real-time page view
metrics with 5-second refresh intervals. Includes WebSocket
integration for live data streaming.

AI-assisted: Chart component scaffolding generated by Claude Code,
modified for accessibility and error handling.

Closes DA-178

PR Requirements

Every merge to main requires a PR with:

  1. At least one reviewer (for the 3-day trial period, Maya reviewed everything; after that, any senior or mid-level developer could review).
  2. PR description using the team template (generated with AI assistance).
  3. All CI checks passing (linting, type checking, tests).
  4. PR size under 400 lines (with an escape hatch for AI-generated test files, which tend to be large).

Merge Strategy: Squash Merge

All PRs use squash merge. This collapses the branch's commit history into a single clean commit on main. The squash commit message must follow Conventional Commits format and be written by the PR author (not auto-generated).

Git Hooks

The team set up the following hooks using the pre-commit framework:

# .pre-commit-config.yaml
repos:
  - repo: https://github.com/pre-commit/pre-commit-hooks
    rev: v4.5.0
    hooks:
      - id: trailing-whitespace
      - id: end-of-file-fixer
      - id: check-yaml
      - id: check-added-large-files
        args: ['--maxkb=500']
      - id: detect-private-key

  - repo: https://github.com/psf/black
    rev: 24.3.0
    hooks:
      - id: black

  - repo: https://github.com/PyCQA/flake8
    rev: 7.0.0
    hooks:
      - id: flake8
        args: ['--max-line-length=88', '--extend-ignore=E203,W503']

  - repo: https://github.com/Yelp/detect-secrets
    rev: v1.4.0
    hooks:
      - id: detect-secrets
        args: ['--baseline', '.secrets.baseline']

  - repo: local
    hooks:
      - id: commit-msg-check
        name: Validate commit message format
        entry: python scripts/validate_commit_msg.py
        language: python
        stages: [commit-msg]
        always_run: true

Branch Protection Rules (GitHub)

main branch:
  - Require pull request reviews: 1 reviewer minimum
  - Require status checks: lint, typecheck, test
  - Require branches to be up to date
  - Restrict push access: no direct pushes
  - Require linear history: squash merge only

Implementation: A Phased Rollout

Phase 1: Foundation (Week 1)

Maya set up the infrastructure:

  1. Created the .pre-commit-config.yaml and installed hooks.
  2. Configured GitHub branch protection rules.
  3. Created PR templates.
  4. Wrote a one-page workflow guide for the team.
  5. Conducted a 1-hour team training session.

The training session covered the new workflow, demonstrated the commit message format, and walked through a complete feature-branch-to-PR cycle. Maya deliberately kept the session short and focused on the "why" behind each practice.

Phase 2: Supervised Practice (Weeks 2-3)

For two weeks, Maya reviewed every PR personally. She provided detailed feedback not just on the code but on the workflow practices:

  • "Great commit message format, but the body should explain why you chose this approach."
  • "This PR is 650 lines. Can we split the test file into a separate PR?"
  • "The AI disclosure in the commit is helpful. Let us also note which parts you modified."

Common issues during this phase:

  • Jordan initially committed AI-generated code without running it first. A pre-commit hook caught a syntax error that would have broken the build.
  • Raj struggled with the 400-line PR limit. His AI sessions generated large modules. Maya helped him learn to break AI output into logical PRs.
  • Priya adopted the workflow quickly and began writing excellent PR descriptions.

Phase 3: Independent Operation (Weeks 4-12)

After two weeks, Maya opened up review responsibilities to Raj and Priya. The team settled into a rhythm:

  1. Pick a ticket from the sprint board.
  2. Create a feature branch.
  3. Use AI tools to generate initial implementation.
  4. Review and refine the AI output.
  5. Commit with proper messages.
  6. Create a PR with AI-assisted description.
  7. Address review feedback.
  8. Squash merge to main.

Results

After three months, Maya re-ran her analysis:

Metric Before After Change
Average commit size 347 lines 89 lines -74%
Commits with meaningful messages 23% 94% +309%
Merge conflicts per week 12 1.5 -88%
Average PR review time N/A 4 hours New metric
Broken deployments per month 8 0.5 -94%
Commits with tests 15% 72% +380%
Developer satisfaction (1-10) 4.2 8.1 +93%

The most dramatic improvement was in deployment reliability. The combination of PR reviews, CI checks, and branch protection rules virtually eliminated broken deployments. The team went from 8 broken deployments per month to less than one.

Merge conflicts dropped by 88%, primarily because: - Short-lived branches (3 days max) reduced divergence. - Daily rebases caught conflicts early when they were small. - Better coordination (via PR descriptions) reduced overlapping work.

Lessons Learned

1. AI Tools Amplify Both Good and Bad Practices

When the team had no workflow discipline, AI tools amplified the chaos by generating more code faster. When they adopted a structured workflow, AI tools amplified productivity by generating code that flowed smoothly through the review and merge process.

2. The 3-Day Branch Limit Was Transformative

Forcing branches to be short-lived changed how the team approached AI-assisted development. Instead of asking an AI to generate an entire feature at once, developers learned to break work into smaller, mergeable increments. This led to better AI prompts (more focused) and better code (more reviewable).

3. Squash Merge Simplified Everything

Squash merge eliminated debates about commit history cleanup. Developers could make as many messy WIP commits as they wanted on feature branches, knowing the history would be collapsed into a single clean commit on main.

4. Pre-commit Hooks Saved the Junior Developer

Jordan's growth was accelerated by the pre-commit hooks. Instead of learning about code quality from PR review feedback (which is slow), they got immediate feedback from hooks on every commit. Within a month, Jordan's code quality had improved significantly.

5. AI Disclosure Built Trust

The team's practice of noting AI assistance in commit messages built trust during code reviews. Reviewers knew which parts to scrutinize more carefully, and the team developed a shared vocabulary for discussing AI-generated code quality.

Key Takeaways for Your Team

  1. Start with a clear branching strategy that accounts for AI-assisted development's faster pace and larger diffs.
  2. Enforce commit conventions through Git hooks, not just documentation.
  3. Require PRs for all merges to main, even on small teams.
  4. Set PR size limits and help team members learn to break AI output into manageable pieces.
  5. Use squash merge for feature branches to keep the main branch history clean.
  6. Roll out changes in phases: infrastructure first, then supervised practice, then independent operation.
  7. Measure before and after to demonstrate the impact of workflow improvements.

Discussion Questions

  1. How would this workflow change if the team grew from 5 to 20 developers?
  2. What would you modify if the team used a monorepo instead of a single repository?
  3. How might the AI disclosure practice affect external contributors or open-source collaboration?
  4. What additional hooks or automation would you add for a team that deploys continuously (multiple times per day)?
  5. How would you handle a situation where a developer consistently exceeds the 400-line PR limit because their AI-generated code is tightly coupled?