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

# Tutorials

These tutorials demonstrate how to build Data Designer configurations and execute them through the NeMo Data Designer plugin.

The code snippets on this page are for conceptual demonstration purposes only.
For runnable examples, jump ahead to the [Basics](/documentation/design-synthetic-data/tutorials/the-basics) or [Seeding](/documentation/design-synthetic-data/tutorials/seeding-with-external-datasets) tutorial.

## Configuration and Execution

Data Designer separates **configuration** (building dataset schemas) from **execution** (generating the data).

**Part 1: Build Configs (Library)**

Use `data_designer.config` to define your dataset. See the [library documentation](https://docs.nvidia.com/nemo/datadesigner/v0.9.1/getting-started/welcome) for comprehensive guides on column types, constraints, and processors.

```python
import data_designer.config as dd

config_builder = dd.DataDesignerConfigBuilder(model_configs)
config_builder.add_column(dd.SamplerColumnConfig(...))
config_builder.add_column(dd.LLMTextColumnConfig(...))
```

**Part 2: Execute (Plugin)**

Run the configuration with the CLI or SDK:

```bash
nemo data-designer preview product_reviews.py --num-records 5
nemo data-designer create product_reviews.py --num-records 30
```

```python
import os
from nemo_platform import NeMoPlatform

client = NeMoPlatform(
    base_url=os.environ.get("NMP_BASE_URL", "http://localhost:8080"),
    workspace="default",
)
data_designer = client.data_designer
preview = data_designer.preview(config_builder)
job = data_designer.create(config_builder, num_records=1000)
```

## Prerequisites

Complete [Setup](/documentation/get-started) to ensure you have the NeMo Services running locally and an inference provider available.
These tutorials reference the default NVIDIA Build model provider, which is created as `default/nvidia-build` during setup.

## Tutorials

#### [The Basics](/documentation/design-synthetic-data/tutorials/the-basics)

Generate a product review dataset using samplers and LLM-generated text. Learn the fundamentals of building configurations and executing jobs.

beginner data-designer

#### [Seeding](/documentation/design-synthetic-data/tutorials/seeding-with-external-datasets)

Use external datasets to ground synthetic data generation. Generate realistic patient medical notes from symptom-to-diagnosis data.

intermediate data-designer