A plugin framework for the custom pieces every real project ends up needing
Data Designer is built around a simple idea: describe the dataset you want, and let the framework handle execution. A config points to seed data, defines generated columns, picks models, and shapes the final records — no orchestration code required. Data Designer plugins keep that promise when a project needs something custom.
How do you teach a VLM to read charts, cross-reference tables, and reason over 100+ page PDFs? We generated ~11.4M synthetic visual question-answer pairs (~45B tokens, including questions, answers, thinking traces, and vision tokens) with NeMo Data Designer to improve long-document visual reasoning in a multimodal model. We used MMLongBench-Doc as our main evaluation target throughout the project, tracking both overall progress and the specific document-reasoning capabilities the model was still missing. In this post, we cover what worked and what didn't.
You just generated 10k multilingual greetings (or some other cool dataset). Now what — email a parquet file?
Nah. Call .push_to_hub() and you've got a live dataset page on Hugging Face. Done and dusted 🚢.
While LLMs have mastered generic coding, Text-to-SQL remains one of the most challenging frontiers in enterprise AI. In many ways this is due to (i) SQL tasks relying on both code and data and (ii) real-world data and databases being quite messy. Focusing on careful data design that accounts for real-world diversity and complexity, we built a NeMo Data Designer pipeline that includes conditional sampling, three-stage LLM generation, code validators, and multi-dimensional judge scoring to generate reasoning-heavy text-to-SQL samples across PostgreSQL, MySQL, and SQLite, and automatically filter down to the highest quality 96.5k records. Each sample pairs a natural-language prompt and a fully synthetic database schema context with a target SQL query. To improve robustness and mimic the messiness of production databases, the pipeline injects distractor tables and columns into the schema context, forcing the model to learn to ignore irrelevant schema elements. The final dataset is validated and filtered through per-dialect syntax validators and five LLM-as-a-critic judges.
Data Designer's execution engine now schedules work at the cell level rather than the column level. Instead of running each column to completion before starting the next, the async engine dispatches a cell as soon as its specific upstream dependencies complete. Multi-model pipelines keep every endpoint saturated, and single-model pipelines benefit from AIMD-based adaptive concurrency. The result is faster pipelines with no changes to your config.
Picture this: you're generating a million-record dataset. Thirty two concurrent requests per model, three models in the pipeline, two providers. Everything hums along for the first ten minutes — then one provider starts returning 429s, your retry logic kicks in, and suddenly you're in a feedback loop where retries cause more 429s. The run stalls. You restart with lower concurrency, waste throughput for hours, and wonder if there's a better way.
There is. This post is about the native model client layer we built with adaptive throttling (a system that discovers provider capacity at runtime) replacing our dependency on LiteLLM along the way.
Lessons from building an agent-first CLI and skill for Data Designer
We just published the data-designer skill, which leverages agent-focused CLI commands in Data Designer to efficiently generate datasets. Just describe the dataset you want and your agent will craft the Data Designer configuration for you — schema design, validation, preview, generation — interactively or on full autopilot (just tell the agent to "be opinionated" or "surprise me").
Training search agents requires trajectory data --- the full multi-turn interaction showing how a model searches, reads, reasons, and answers. We built a four-stage pipeline that generates synthetic search trajectories from Wikidata knowledge graph paths, converts them into BrowseComp-style riddles using NeMo Data Designer, generates multi-step search rollouts with live web search via Tavily, and post-processes the results into SFT-ready training data.
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.
