What matters when synthetic training data is generated on demand?

Disclosure: I work on Abliteration, and we just launched a made-to-order training data workflow. One practical issue we kept seeing: teams need negative, rare, and adversarial examples for classifiers, but those examples are often exactly what general-purpose models refuse to produce. That makes safety classifiers, abuse detection, jailbreak evals, and security research datasets harder to build than they should be. For generated training data to be useful, I think it needs more than a prompt box: \- a target schema before generation starts \- a way to mix in current or real-world facts when needed \- labels and reason codes that survive export \- enough provenance to review a dataset later \- export paths into the tools people already use The thing we launched lets you describe the examples you want, optionally use web search, and export to Hugging Face, Kaggle, S3, or OpenAI. Initial use cases include moderation classifiers for grooming and harassment, security-research datasets, and model evals. Product: [https://abliteration.ai/](https://abliteration.ai/) Synthetic data page: [https://abliteration.ai/use-cases/synthetic-data](https://abliteration.ai/use-cases/synthetic-data) Launch/video: [https://x.com/abliteration\_ai/status/2054675554138194178](https://x.com/abliteration_ai/status/2054675554138194178) Curious how people here think about reviewability. If a generated dataset is going into a classifier, what would you want logged for each row?