Grounded synthetic data starts from real references

The main risk with synthetic data is not that it is synthetic; it is that it is ungrounded. A model asked to invent training examples from scratch will often produce generic, high-probability patterns that miss the domain's real constraints. Grounded generation starts from real references and uses generation to add scale and variation around them.

NVIDIA NeMo Data Designer grounds generation in text, structured, RAG, instruction, and agent workflows. Teams attach trusted seeds (product catalogs, policies, support cases, domain examples, code snippets, task taxonomies, document passages, or SME-written scenarios), and their column values become variables injected into the generation prompts, so every record is anchored to something real. The workflow should include validation, judge scoring for that workflow, preview, and iteration before scaling. NVIDIA's published Nemotron-Personas datasets are one example of open data grounded in real population distributions rather than invented from scratch.

For physical AI, grounding may come from CAD files, 3D assets, real-to-sim reconstruction, real sensor data, or measured deployment conditions. NVIDIA Isaac, NVIDIA Omniverse Replicator, and NVIDIA Cosmos workflows may be relevant for those physical AI use cases. For sensitive tabular data, Safe Synthesizer may be relevant when the real examples are private records and the goal is a safe synthetic stand-in, it is not a generic text or document generator.

Grounding is especially important for agents. Agent data should be grounded in real workflows: what users ask for, what tools exist, what permissions apply, what evidence is available, what errors happen, and when escalation is required. Without that grounding, the agent learns a fictional workflow that may fail immediately in production.

Key takeaways

• Grounded synthetic data starts from real references, not a blank prompt.
• NVIDIA NeMo Data Designer supports seed-grounded generation for synthetic data workflows.
• Grounding sources can include examples, documents, rules, schemas, simulator assets, or workflow traces.
• Agent data should be grounded in real tools, permissions, retrieval sources, policies, and failure modes.