Using NeMo Data Designer, an orchestration framework for generating high-quality synthetic data at scale, we built an iterative pipeline that generates diverse, schema-constrained structured outputs across JSON, YAML, and XML. Through multiple rounds of prompt refinement, rejection sampling, and programmatic validation, we produced a 9,949-sample dataset of verified structured output training data.
Data Designer v0.5.0's MCP tool-use support lets you generate multi-turn research trajectories, the kind of data needed to train deep research agents that iteratively search, read, and synthesize evidence before answering a question.
Synthetic data generation is more than a single prompt to a large language model. In this post, we walk through the design principles behind NeMo Data Designer and explain why we built it as a composable orchestration framework - treating SDG as a system of specialized stages rather than a monolithic generation task.
Using NeMo Data Designer, we created the RQA (Reasoning Question-Answer) dataset: a massive collection of graduate-level, reasoning-heavy science samples designed to push the boundaries of model performance.