How to Record AI Media Edit Settings for Reproducible Results

A useful AI media run record captures the prompt, settings, exact model version, API version when applicable, and deployment context. These details help teams identify changes and compare runs, although they do not guarantee identical outputs.

Why the exact model version belongs in every run record

Recording a model name alone can leave an important gap. Microsoft distinguishes the version of a deployed model from the API version used to access it: one deployment can support multiple API versions. A useful record should therefore keep these as separate fields.

Version tracking also matters because deployments may follow different update policies. Microsoft Foundry deployments can opt out of automatic upgrades, move when a new default version becomes available, or wait until the current version expires. Microsoft warns that a version upgrade can affect behavior or compatibility and recommends testing applications and workflows after a new version is released. Consequently, an unchanged prompt and visible settings do not establish that two runs used the same underlying model version.

A practical record for prompts, versions, settings, and outputs

The following checklist is editorial guidance, not an official platform specification. For each image or video edit, record:

• The complete prompt text, including any separate instructions.
• The model name and exact deployed model version.
• The API version, when an API is involved.
• Every adjustable setting shown or submitted for the run.
• The run date and a stable internal reference to its output.
• The deployment or environment used.
• The region and deployment type, when relevant.
• The deployment’s upgrade policy, if known.

Keep each run as a separate entry instead of overwriting an earlier record. This makes changes visible during comparison and handoff. Deployment context deserves its own fields because Azure Government documentation says automatic upgrades depend on availability within the same deployment type and region. The supplied sources do not define a complete record for generative-media editing, so teams should adapt this checklist to the controls their workflow actually exposes without treating it as a vendor requirement.

How to compare runs and manage upgrades

Start a comparison by matching the prompt and recorded settings, then compare the exact model version separately from the API version. In Microsoft Foundry, the current model version can be checked in deployment details or obtained through the deployments REST endpoint. Next, compare the environment, region, deployment type, and upgrade policy to identify contextual differences.

If the model version changed, treat the later run as a new test condition. Microsoft recommends retesting workflows after a new version is released because upgrades can change behavior or compatibility. Preserve the earlier entry, document the newer version, and attach comparison notes rather than rewriting history.

For general version-management practice, the dbt model-versioning proposal illustrates the value of specific version references, deprecation dates, simultaneous availability of versions, and migration windows for downstream users. That source concerns data models, not generative media, so it supports only the broader migration principle. For AI media work, retain any available retirement or migration notes alongside the run record so the team can see when a pinned version may require reassessment.

Conclusion

At minimum, record the prompt, settings, exact model version, and API version as distinct fields. Add the date, output reference, environment, region, deployment type, and upgrade policy when they apply. This practical record supports comparison and migration work but does not promise identical outputs or represent an official platform standard. Create a reusable template now, complete it for your next AI media edit, and compare its model and deployment details before attempting to reproduce the run.

Disclosures and limitations

• This article was prepared with AI assistance and is based on the supplied Microsoft, Azure Government, and dbt source material. The proposed run-record checklist is editorial guidance, not a platform-mandated specification.
• No products are recommended in this article, and no affiliate relationship or purchasing incentive informed its guidance.

Sources

• Model versioning in Microsoft Foundry Models – Microsoft Foundry — learn.microsoft.com
• Model versions · dbt-labs/dbt · Discussion #6736 — GitHub
• Model versioning in Microsoft Foundry Models in Azure Government – Microsoft Foundry — learn.microsoft.com