Phase 3: The Generate-Validate Loop · 40 min · Git · Claude Code · Python
Git as a Safety Net for AI Code
Commit after every validated step, not at the end of the session — the commit is what makes 'that went wrong, revert it' possible instead of theoretical.
Real-world application: Demonstrating that you commit AI-generated work incrementally, with real messages and traceability, is concrete evidence of the audit discipline production teams actually require before trusting agent-written code.
What you will learn
- Commit after each validated generation step so a bad step can be reverted precisely, without losing prior progress
- Read a diff and restate in plain English what changed and why, as a validation check before accepting it
- Use branch-per-feature to limit the blast radius of an exploratory or risky AI-assisted session
- Audit a commit log for clear messages and traceability back to the spec or task that motivated each change
Introduction
Git as a Safety Net for AI Code
Objective
Commit a sequence of generation steps to a real repository, revert precisely to the last known-good commit after a bad step, and audit the commit log for message quality.
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You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.
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