> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/nemo/datadesigner/llms.txt.
> For full documentation content, see https://docs.nvidia.com/nemo/datadesigner/llms-full.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nemo/datadesigner/_mcp/server.

# Use Case Recipes

> Self-contained code examples that demonstrate how to leverage Data Designer for specific use cases.

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.

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](/tutorials/overview), 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.

These recipes use the OpenAI model provider by default. Ensure your OpenAI provider is set up via the Data Designer CLI before running a recipe.

## 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

Read agent rollout traces from disk and turn each one into a structured workflow record inside a Data Designer pipeline. See the [ingestion guide](/concepts/agent-rollout-ingestion) for the trace format.

*`AgentRolloutSeedSource` · ATIF, Claude Code, Codex, Hermes formats · trace-aware prompts*

## 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

Define a custom `FileSystemSeedReader` inline and turn Markdown files into one seed row per heading section.

*Single-file custom reader · `hydrate_row()` fanout · `DirectorySeedSource` customization*