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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallA run manifest records which prompt revision produced an AI result, alongside the inputs, model settings, code, and evaluation context needed to interpret it. Treat prompts as versioned application code: review each change, test it against representative cases, and make every production run point to the exact revision—not a moving “latest” label.
What a run manifest should tell you
A manifest is a per-run record that connects an output to the prompt and conditions that produced it. There is no universal schema established by the cited documentation; the fields below are a practical design recommendation. Keep sensitive data protected: a manifest can identify an input fixture or refer to a controlled record instead of copying private values into logs.
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- Run identity: a unique run ID, UTC start time, environment, and reference to the trace or output record.
- Prompt identity: prompt name, immutable revision ID or content hash, and either the exact rendered prompt or a controlled reference. If a label can move, record the underlying revision identifier too.
- Input identity: variable names and values when appropriate, or a privacy-safe fixture reference. Record relevant retrieval dataset or document snapshot identifiers if retrieval can affect the result.
- Model and execution configuration: provider, model identifier or version when available, sampling and output settings, tool definitions or versions, and application or code revision.
- Evaluation context: dataset or fixture version, evaluator or rubric revision, scores and failures, and links to detailed results.
- Change context: change owner, review or release reference, concise change note, deployment cohort or feature flag where relevant, and rollback target.
A Git commit or release tag can connect the prompt to application history. Do not claim full reproducibility when a provider does not expose a stable model version or a downstream data source cannot identify a fixed snapshot.
How to track a prompt change through deployment
- Store recoverable prompt revisions. Keep prompt content in a named, versioned module or an equivalent system that retains prior revisions. Separate stable instructions from validated input parameters when practical. OpenAI’s API guide says to “Treat prompts as application code” and recommends reviewing prompt changes alongside product behavior. OpenAI API prompting guide.
- Review the edit as a behavior change. Record what changed and why in the same review or release record as affected application behavior. Preserve the code history so the prompt used by a run can be connected to its application revision.
- Evaluate the candidate revision. Run representative fixtures and the relevant evaluation set. Retain the prompt revision, dataset or fixture reference, evaluator version, and results together. OpenAI recommends tests and evaluation checks as part of deployment. Its Playground documentation describes linked evaluations, but says reruns are manual. OpenAI Playground prompt management.
- Compare against the deployed revision. Assess output quality and task-specific failures. Where operationally important, also compare latency, cost, safety behavior, and tool-use errors; these are useful comparison dimensions, not metrics prescribed by the cited versioning documentation.
- Promote exactly what passed. Deploy the tested revision, have production runs record its revision identifier and execution context, and retain a rollback reference. Git history, pull-request review, release tags, and feature flags can support review, shipping, comparison, and rollback.
- Investigate from the manifest. Use the run ID to retrieve its prompt revision, code revision, input or safe input reference, and evaluation context. Note unresolved uncertainty when model identifiers, retrieval data, or other dependencies lack stable version information.
Choose revision controls that preserve exact identity
A hosted prompt interface can make editing and comparing convenient, while code-managed prompts can fit an application’s existing review and release process. Choose based on revision identity, release control, evaluation linkage, rollback, access controls, data handling, audit needs, and whether non-engineers need to edit prompts. The documented capabilities do not establish a universal best choice or comparative performance advantage.
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| Approach | What the cited documentation establishes | Manifest implication |
|---|---|---|
| OpenAI Playground prompt versions | Drafts can be published as versions; earlier versions can be restored; versions can be pinned and compared side by side; an Eval can be linked. A Prompt ID called without a version resolves to the latest published version. Linked evaluation reruns are manual, according to the help article. | Record the pinned version for a run that must target a specific revision; do not rely on an unqualified Prompt ID when exact attribution matters. |
| Code-managed prompts | OpenAI’s API guide recommends code-managed, versioned prompt helpers and review alongside product behavior, with tests and evaluation checks. | Record the code commit or release reference and a prompt revision identifier or hash that remains tied to that code history. |
| LangSmith prompt commits | Saved updates are commits with unique hashes; historical versions can be reviewed or reverted; human-readable tags are available. A tag can be reassigned. | Store the commit hash when exact identity matters; a tag alone may later refer to a different commit. LangSmith prompt management documentation. |
Evaluation results are meaningful only alongside the prompt revision, test data, and evaluation method that produced them. Keep those references together rather than treating a score as a property of the prompt name alone.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.OpenAI prompt-object lifecycle dates
OpenAI’s current API guide says reusable prompt objects are being deprecated, prompt creation will be de-emphasized beginning June 3, 2026, and the v1/prompts endpoint is scheduled to shut down November 30, 2026. The guide directs teams already using prompt IDs or versions to a migration guide. These are lifecycle dates, not performance measures; verify the current guidance before planning a migration or implementation because timelines can change. OpenAI API prompting guide.
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