Prompt Versioning
Prompt versioning keeps named, fixed revisions of a production prompt. Teams can review, test, release, and restore an exact revision.
revision → eval → release → rollbackowner: support-ai · rev 7f2aA support team changes refund-prompt.md from v6 to v7. It tests both on saved cases, sends some requests to v7, and returns to v6 if results get worse.+ clearer rule48 / 4810% → v7prompt file + revision + eval + release reference + rollbackOPSAnother example
A support team changes refund-prompt.md from v6 to v7. It tests both on saved cases, sends some requests to v7, and returns to v6 if results get worse.
Main parts
- 01Prompt file or key
- 02Locked revision
- 03Change diff and owner
- 04Evaluation result
- 05Release reference
- 06Rollback reference
Use it when
Use it when a production prompt changes with code and needs review, tests, release, or rollback.
Do not use it when
Do not edit a production prompt without saving the exact old and new revisions. Each change still needs review and tests.
Name used in code
prompt file + revision + eval + release reference + rollback Before you ship
Check the model and prompt versions, source data, versioned evaluation set, and measures. Verify links to evidence, tool permissions, privacy, common failures, decline-to-answer and fallback behavior, human review, monitoring, cost, speed, and rollback.
Outcome: Use or evaluate Prompt Versioning to make AI behavior measurable and tied to evidence. User context: A support team changes refund-prompt.md from v6 to v7. It tests both on saved cases, sends some requests to v7, and returns to v6 if results get worse. AI method or concept: Prompt Versioning. Why it fits: Use it when a production prompt changes with code and needs review, tests, release, or rollback. Do not use it when: Do not edit a production prompt without saving the exact old and new revisions. Each change still needs review and tests. AI requirements: Define the input and source evidence. Set model and tool permissions. Use versioned evaluation data and measures. Define failure, decline-to-answer, privacy, speed, and cost limits. Operational safeguards: Keep a clear trace and hide sensitive log data. Show users a safe fallback. Mark steps that need human review. Define how to roll back the model or prompt. Acceptance criteria: Record baseline and target measures on a versioned evaluation set. Test edge cases and hostile inputs. Verify fallback, monitoring, permissions, and rollback. Evidence and limits (evidence boundary): It is official for that source. Other platforms or teams may use the term in another way. Unknowns to confirm: Target task, model and version, evaluation owner, source data, risk limit, tool permissions, and production fallback.
Check this request
Review the current use of Prompt Versioning. Definition: Prompt versioning keeps named, fixed revisions of a production prompt. Teams can review, test, release, and restore an exact revision. Release checks: Check the model and prompt versions, source data, versioned evaluation set, and measures. Verify links to evidence, tool permissions, privacy, common failures, decline-to-answer and fallback behavior, human review, monitoring, cost, speed, and rollback. Before changing code, report the evidence you found, gaps, severity, and the smallest safe fix.