Chapter 31: Key Takeaways

Version Control Workflows — Summary Card

1. AI-assisted development requires more version control discipline, not less. The speed of AI code generation makes frequent commits, small PRs, and thorough reviews more important than ever. Every AI interaction should be bookended by commits to create reliable rollback points.

2. Choose your branching strategy based on team dynamics and AI usage intensity. Trunk-based development pairs well with fast-paced AI-assisted work and strong CI/CD. Feature branches provide safety for larger AI-generated changes. Gitflow adds overhead that is rarely justified for AI-heavy teams.

3. Adopt Conventional Commits as your commit message standard. The structured format (type(scope): description) creates machine-parseable history that enables automated changelog generation, semantic version bumping, and meaningful git log output. When AI generates code, the commit message is your primary record of human intent.

4. Never blindly git add . after an AI session. Use git diff to review all changes, then git add -p to selectively stage only the code you have reviewed and approved. AI tools may introduce debug code, hardcoded values, or changes to files you did not intend to modify.

5. Keep pull requests under 400 lines of changes. Break large AI-generated outputs into multiple logical PRs. Smaller PRs receive more thorough reviews, merge with fewer conflicts, and are easier to revert if problems are discovered.

6. Squash merge is the best default strategy for AI experimentation branches. It collapses messy WIP commits into a single clean commit on the main branch, preserving a readable history while allowing developers to experiment freely on feature branches.

7. Use AI to assist with Git operations, not to replace your judgment. AI tools can draft commit messages, generate PR descriptions, help resolve merge conflicts, and automate changelog generation. But always review AI output for accuracy, and add the human context (the "why") that AI cannot provide.

8. Prevent merge conflicts through frequent integration. Rebase feature branches on main at least every two days. Use weekly integration branches to detect conflicts early. The cost of delayed integration grows non-linearly.

9. Implement Git hooks as automated quality gates. Pre-commit hooks (linting, formatting, secret detection), commit-msg hooks (conventional format validation), and pre-push hooks (test execution) catch problems before they enter the repository. Move slow checks (full test suites) to CI rather than pre-commit to avoid developer frustration.

10. Monorepos benefit AI-assisted development through unified context. AI tools work better when they can see the entire codebase, understand cross-component relationships, and make atomic changes across package boundaries. Choose a monorepo when components share types, utilities, or configuration.

11. Automate release management with semantic versioning. Combine Conventional Commits with automated version bumping and changelog generation. AI can generate human-readable release notes from structured commit history.

12. The reflog is your ultimate safety net. Almost nothing in Git is truly permanent. If a rebase goes wrong, a branch is accidentally deleted, or a reset loses changes, git reflog lets you find and recover the previous state within 90 days.

13. Treat AI-generated code in reviews like code from a talented junior developer. Watch specifically for plausible but incorrect logic, outdated patterns, security vulnerabilities, missing error handling, and over-engineering. Avoid the "LGTM trap" of rubber-stamping syntactically correct AI output.

14. Document AI involvement in your version history. Whether through commit message conventions, PR description templates, or branch naming prefixes, create a clear record of which code was AI-generated. This metadata helps future developers understand code provenance and calibrate their trust.

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