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The core problem Git solves is how to record, exchange, review, and recover changes during multi-person collaboration.
If you only treat Git as a “command for uploading code”, you will easily get flustered during conflicts, rollbacks, branch collaboration, and production incidents. To truly understand Git, you must view it as an engineering collaboration system.
Every commit is a traceable engineering record.
A good commit can answer:
Branches allow different tasks to remain independent.
You can simultaneously process:
As long as branch boundaries are clear, team collaboration will not be blocked for everyone due to one person’s local changes.
When Git is combined with GitHub / GitLab, Pull Requests become the entry point for team review.
Teams can check before merging:
Git is responsible not only for saving code but also for incident recovery.
Common scenarios include:
For how to handle these issues, see the Git Troubleshooting Playbook.
AI speeds up code generation, but it also makes the scale of changes larger and boundaries more prone to blurring.
Git’s new value is slicing large AI-generated changes back into engineering units that humans can understand, review, and roll back.
If you are using Codex, Claude Code, Cursor, Cline, or other AI programming tools, read AI Native Git Workflow first.
Master this main line first:
working tree -> index -> local repository -> remote repository
Then master three types of capabilities:
For the original article, see the legacy directory why-git.md.