Use Case Recipes
Recipes are a collection of code examples that demonstrate how to leverage Data Designer in specific use cases. Each recipe is a self-contained example that can be run independently.
New to Data Designer?
Recipes provide working code for specific use cases without detailed explanations. If you’re learning Data Designer for the first time, start with our tutorial notebooks, which offer step-by-step guidance and explain core concepts. Once you’re familiar with the basics, return here for practical, ready-to-use implementations.
Prerequisites
Most recipes use the OpenAI model provider by default. Ensure your OpenAI provider is set up via the Data Designer CLI before running model-backed recipes. Image-generation recipes use OpenRouter and Gemini image models by default, so set OPENROUTER_API_KEY before running them unless you override the model provider and model ID. Headless recipes, such as Document Review Gate, do not call a model provider.
Image Generation
Generate synthetic business-document page images with controlled metadata for VQA, OCR, multimodal judging, and document-understanding workflows.
Image generation · visual seed data · VQA-ready parquet export
Use image-to-image generation to create inclusive adult apparel catalog variants across age groups, ethnicities, body types, poses, and styling contexts.
Image-to-image · apparel catalog · inclusive representation
Generate synthetic dog and cat photos, then use image-to-image generation to make the same pet scene funnier while preserving identity.
Image-to-image · creative review · identity preservation
Generate self-driving car ego-camera scenes with controlled road, weather, lighting, traffic, and long-tail hazard variation.
AV ego camera · edge cases · visual review sets
Generate research-only extremity X-ray style images with controlled anatomy, acquisition, finding, and quality metadata.
Research only · visual QA · report generation
Generate defensive baggage-screening style images with controlled clutter, material mix, scanner style, and review labels.
Defensive evaluation · human review · scanner-like images
Generate egocentric humanoid robot scenes with controlled environment, viewpoint, task, object, safety, lighting, and human-presence metadata.
Embodied AI · scene understanding · safety review
Generate crop disease detection images with controlled crop, growth stage, viewpoint, condition, severity, and field context.
Crop disease detection · healthy negatives · reviewer calibration
Generate low-altitude drone inspection images for infrastructure, property, construction, disaster-response, and industrial review workflows.
Drone inspection · infrastructure QA · reviewer calibration
Code Generation
Natural-language instructions paired with Python implementations across complexity levels and industries.
Python code generation · validation · LLM-as-judge
Natural-language instructions paired with SQL implementations across complexity levels and industries.
SQL code generation · validation · LLM-as-judge
Enterprise-grade text-to-SQL training data — dialect-specific SQL, distractor injection, dirty data, 5 LLM judges with 15 scoring dimensions.
Multi-dialect SQL · SubcategorySamplerParams · 5 judges · 15 score columns
QA and Chat
Product information paired with question/answer pairs.
Structured outputs · expression columns · LLM-as-judge
Multi-turn chat conversations between a user and an AI assistant.
Structured outputs · expression columns · LLM-as-judge
Trace Ingestion
Workflow Chaining
MCP and Tool Use
Minimal example of MCP tool calling — defines a simple MCP server and generates data that requires tool calls to complete.
LocalStdioMCPProvider · simple tool server · tool-augmented text
Grounded Q&A pairs from PDF documents using MCP tool calls and BM25 search.
LocalStdioMCPProvider · BM25 retrieval · per-column trace capture
Multi-turn search agent trajectories — Tavily web search via MCP, Wikidata KG seeding, BrowseComp-style question generation.
Tavily MCP · Wikidata seeding · two-stage question generation · trajectory capture
Plugin Development
VLM Long-Document Understanding
A 9-recipe pipeline for generating visual QA training data from long PDF documents: OCR, page classification, single-page / multi-page / whole-document QA, and frontier-model quality filtering.
Download PDFs, render page images, and prepare seed datasets for the downstream VLM recipes.
Run Nemotron Parse over document pages and save OCR transcripts for text-based QA generation.
Generate text-grounded question-answer pairs from OCR transcripts.
Classify pages by visual reasoning potential before running more expensive QA generation.
Generate visual question-answer pairs from classified page images.
Generate single-page VLM QA examples from page-level image seeds.
Generate cross-page QA examples over fixed-size page windows.
Generate document-level QA examples over grouped page images.
Score and filter generated QA pairs with a stronger independent judge.