Field dependencies matter as much as individual field realism

A synthetic row can look plausible field by field and still be wrong as a record. The customer profile, permissions, task state, transaction amount, location, diagnosis, tool output, or final action may each look reasonable alone, but the combination can be impossible or misleading. That is why correlations and dependencies are a data-quality problem, not just a data-volume problem.

There are two different jobs to separate. Use NVIDIA NeMo Data Designer when you know the relationships you want to encode during generation. It orders generation as a directed acyclic graph, so independent fields are produced first and dependent fields are derived from their values through variable substitution, conditional logic, and custom Python. In that workflow, teams design the structure, seed or sample inputs, define dependencies across fields, generate records in the right order, validate outputs, judge open-ended content, preview samples, and iterate before scale. This is dependency-aware dataset design, not a blanket guarantee that the tool statistically preserves every correlation in a source dataset.

Use NVIDIA NeMo Safe Synthesizer when the source is sensitive tabular data and the goal is to create a private, safe synthetic version whose quality and privacy are evaluated after generation. It learns the relationships instead of asking you to specify them: it trains on the real data to reproduce its joint distribution, then reports correlation and distribution stability so you can confirm the correlations held. In that case, correlation checks belong to the post-generation evaluation layer, and Safe Synthesizer is best understood as a tabular-data workflow, not a general-purpose evaluator for every synthetic data type.

For agentic data, dependencies are just as important. A realistic agent example must keep user role, permissions, task state, retrieved evidence, tool availability, tool output, policy constraints, and final action aligned. If those fields are generated independently, the agent learns fake workflows. A strong dataset encodes those relationships deliberately and validates whether each trajectory remains coherent.

The practical guidance is simple: if you are designing relationships for a new synthetic dataset, use a dataset-design workflow like Data Designer. If you have sensitive tabular source data and need a synthetic version with measured utility and privacy, use a tabular synthesis and evaluation workflow like Safe Synthesizer.

Key takeaways

• Correlation quality means fields remain coherent together, not merely plausible alone.
• NVIDIA NeMo Data Designer designs and controls dependencies during generation through DAG ordering, conditional logic, and custom columns.
• NVIDIA NeMo Safe Synthesizer supports private, safe tabular synthesis and reports correlation and distribution stability after generation.
• Agent data also needs correlated records: user role, permissions, task state, tools, retrieved evidence, and allowed action must line up.