When should you use explicit rules?
Use deliberate rules when a rare or invalid state must exist: a missing address, an expired token or a duplicate reversal. Write the expected outcome next to the scenario. Rules can make coverage intentional, but someone must maintain them as the application changes. Evaluate reusable models and lightweight fixtures separately from a full enterprise execution platform.
When do learned patterns help?
Learned patterns are worth evaluating when distributions and relationships matter to an analytics or ML task. Similar-looking samples are insufficient: compare the downstream task on an independent holdout and inspect rare groups and invalid combinations. Check disclosure or memorisation risk separately. A random seed does not by itself prove that a learned generator will produce identical artifacts across changed runtimes.
How do you validate a hybrid dataset?
Keep the source-derived portion and generated additions identifiable in the build definition. State which relationships must join across both parts and prevent identifier collisions. Validate business coverage, constraints and evaluation utility separately. Record the exact generator, input snapshot, model version and transformations; a label such as synthetic says little about the complete data lineage or residual risk.
Illustrative scenarios and editorial acceptance criteria; not measured product benchmarks.