Description
Training specialized agentic systems, such as reasoning large language models, multi-agent systems and multimodal AI assistants, requires large, high-quality datasets that are often scarce, siloed across an organization, or too sensitive to share. NVIDIA's synthetic data generation tools, built around NeMo Data Designer, are designed to close that gap by creating diverse, domain-specific datasets at scale for any domain.
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Building a dataset starts with configuring the models that will power generation, using aliases for easy reference and tunable inference parameters. From there, a project can be seeded with existing real-world data so the synthetic output keeps the patterns, distributions and characteristics of that data. The next step defines the columns that structure the dataset, including LLM-generated columns driven by prompts and structured outputs. A small preview sample can be generated and reviewed before scaling up to a full dataset, and NeMo Data Designer includes tools to validate generated code and assess overall data quality using automated metrics and LLM-based judges.
The tools cover several use cases. For agentic AI, they generate text-based data to train and evaluate models that reason, plan and take goal-driven actions. For conversational AI, they create dialogue data that captures domain-specific language, intent variations and rare edge cases beyond what scarce real-world transcripts provide. For evaluation and benchmarks, they build domain-specific question-answer datasets to measure retrieval-augmented generation (RAG) performance and compare multiple models on the same task. For low-resource adaptation, they generate realistic text for proprietary coding languages or underrepresented languages. For sensitive data, NeMo Safe Synthesizer creates privacy-safe versions of data with configurations aimed at regulations such as HIPAA and GDPR. The tools also support synthetic structured documents, including tax forms, legal documents and mortgage approvals, for training models on structured data applications.
This is aimed at teams building agentic AI, conversational AI or RAG systems who need training or evaluation data without relying solely on scarce, siloed or sensitive real-world datasets. Pricing and packaging are not detailed on this page; access is through NVIDIA NeMo.
- CategoryMachine Learning Platforms
- Pricing ModelContact sales
- DeploymentCloud
- PlatformsWeb
Synthetic Data Generation for Agentic AI's Core Features
Configure and preview synthetic dataset designs before generating at scale
Seed generation with real-world data to preserve domain patterns
Define custom schemas and LLM-generated columns with structured outputs
Generate domain-specific dialogue data for conversational AI training
Build QA benchmark datasets for RAG evaluation and side-by-side model comparison
Create privacy-safe versions of sensitive data with NeMo Safe Synthesizer
Generate synthetic structured documents such as tax forms and legal documents
Validate generated code and assess dataset quality with automated metrics and LLM-based judges
Support low-resource and proprietary-language text generation
How to use Synthetic Data Generation for Agentic AI?
Access NeMo Data Designer: Open NVIDIA NeMo Data Designer to start a synthetic data project.
Configure models: Connect and alias the models that will generate your dataset, and tune inference parameters.
Add seed data: Upload existing real-world data to ground and diversify the synthetic output.
Define columns: Set up the schema, prompts and structured outputs for each column.
Preview and refine: Generate a small sample, check the results and adjust the design.
Generate and evaluate: Scale up to a full dataset, then validate it with automated metrics and LLM-based judges.
Synthetic Data Generation for Agentic AI's Use Cases
- Agentic AI training
- Conversational AI data
- RAG evaluation
- Privacy-safe datasets
