A Generative AI Engineer is managing prompt templates using MLflow v3.

The Prompt Registry preserves identifiable prompt versions, allowing the team to recover the exact template associated with a historical execution. Linking inference activity to the prompt’s name and version establishes which registered template the application actually used. MLflow also supports lineage between prompt versions, application versions, and execution traces. However, satisfying the entire audit requires retaining the actual runtime variable values or the fully rendered prompt in the relevant execution records. Registry history alone preserves the template, not every value substituted into it. Therefore, D is the best option, provided the logging captures those inputs. Model webhooks, experiment tags, or table history alone do not provide the same explicit prompt-version lineage and complete historical reconstruction. Databricks documentation