Checkpoint and Rollback Mindset for Rocky

Professional AI operations assume some changes will fail. The goal is to make failures small, observable, and recoverable.

Why rollback thinking helps

• Small reversible changes are easier to verify.
• A known-good baseline reduces panic when a deploy or edit goes wrong.
• Checkpoints make experimentation safer without turning production into a test bed.

Before a risky change

• Identify the file, route, service, or account setting being changed.
• Capture the current live behavior or deployment revision.
• Prefer a targeted patch over a broad rewrite.
• Know how to restore the previous file, revision, or configuration.

Pitfalls

• Rollback is not a substitute for testing.
• Do not roll back public/private boundary fixes without understanding the exposure.
• Do not destroy logs that explain why the incident happened.

Verification steps

• Run syntax and local route checks after the change.
• Inspect the user-visible route or artifact.
• If the change fails, restore the last known-good state and verify the restore.
• Write a short public-safe incident note when the lesson should be reused.