Before You Start

Do not explore the workspace first. The workflow's Learn step gives you everything you need.

Goal

Build a synthetic dataset using the Data Designer library that matches this description:

$ARGUMENTS

Workflow

If the user wants Nemotron-style embedding or reranking data (corpus → Q&A → training.jsonl / eval_beir → fine-tune), stop. Announce a handoff to nemo-retrieval-recipes. Do not enter Autopilot or Interactive create. Dedicated jobs are documented in references/retrieval-sdg.md for Stage 0/1 debugging only.

Use Autopilot mode if the user implies they don't want to answer questions — e.g., they say something like "be opinionated", "you decide", "make reasonable assumptions", "just build it", "surprise me", etc. Otherwise, use Interactive mode (default).

Read only the workflow file that matches the selected mode, then follow it:

• Interactive → read workflows/interactive.md
• Autopilot → read workflows/autopilot.md

Rules

• Keep all columns in the output by default. The only exceptions for dropping a column are: (1) the user explicitly asks, or (2) it is a helper column that exists solely to derive other columns (e.g., a sampled person object used to extract name, city, etc.). When in doubt, keep the column.
• Do not suggest or ask about seed datasets. Only use one when the user explicitly provides seed data or asks to build from existing records. When using a seed, read references/seed-datasets.md.
• When the dataset requires person data (names, demographics, addresses), read references/person-sampling.md.
• If a dataset script that matches the dataset description already exists, ask the user whether to edit it or create a new one.
• Data Designer runs on NeMo Helix: inference, seed data, and persona data all resolve through the platform, never from local files or a local model registry. Read references/platform-execution.md before writing a config — it covers model configs, validation, and what the platform accepts.
• For Nemotron retrieval SDG Stage 0/1 (retrieval-generate / retrieval-prepare / retrieval-run), read references/retrieval-sdg.md. Do not use create for that pipeline. Full embed/rerank recipes belong to nemo-retrieval-recipes.

Usage Tips and Common Pitfalls

• Sampler and validation columns need both a type and params. E.g., sampler_type="category" with params=dd.CategorySamplerParams(...).
• Jinja2 templates in prompt, system_prompt, and expr fields: reference columns with {{ column_name }}, nested fields with {{ column_name.field }}.
• SamplerColumnConfig: Takes params, not sampler_params.
• LLM judge score access: LLMJudgeColumnConfig produces a nested dict where each score name maps to {reasoning: str, score: int}. To get the numeric score, use the .score attribute. For example, for a judge column named quality with a score named correctness, use {{ quality.correctness.score }}. Using {{ quality.correctness }} returns the full dict, not the numeric score.

Troubleshooting

• nemo data-designer CLI not found: Tell the user that nemo data-designer is not installed in this environment (requires Python >= 3.11). Ask if they would like you to create a virtual environment and install it, or if they prefer to do it themselves. Do not install anything without the user's permission.
• Network errors during preview: A sandbox environment may be blocking outbound requests. Ask the user for permission to retry the command with the sandbox disabled. Only as a last resort, if retrying outside the sandbox also fails, tell the user to run the command themselves.

Output Template

Write a Python file to the current directory with a load_config_builder() function returning a DataDesignerConfigBuilder. Name the file descriptively (e.g., customer_reviews.py). Use PEP 723 inline metadata for dependencies.

# /// script
# dependencies = [
#   "data-designer", # always required
#   "data-designer-nemo", # only if this script imports a platform seed source
#   "pydantic", # only if this script imports from pydantic
#   # add additional dependencies here
# ]
# ///
import data_designer.config as dd
from pydantic import BaseModel, Field

# Seed data on NeMo Helix comes from the Files service:
# from data_designer_nemo.fileset_file_seed_source import FilesetFileSeedSource


# Use Pydantic models when the output needs to conform to a specific schema
class MyStructuredOutput(BaseModel):
    field_one: str = Field(description="...")
    field_two: int = Field(description="...")


# Use custom generators when built-in column types aren't enough
@dd.custom_column_generator(
    required_columns=["col_a"],
    side_effect_columns=["extra_col"],
)
def generator_function(row: dict) -> dict:
    # add custom logic here that depends on "col_a" and update row in place
    row["name_in_custom_column_config"] = "custom value"
    row["extra_col"] = "extra value"
    return row


def load_config_builder() -> dd.DataDesignerConfigBuilder:
    config_builder = dd.DataDesignerConfigBuilder(
        # Model configs must be declared here. `provider` is an Inference Gateway
        # provider; the one below is a common default created during `nemo setup` —
        # confirm it (or discover others) with `nemo inference providers list`.
        # See references/platform-execution.md.
        model_configs=[
            dd.ModelConfig(
                alias="text",
                model="...",
                provider="default/nvidia-build",
                inference_parameters=dd.ChatCompletionInferenceParams(),
            ),
        ],
    )

    # Seed dataset (only if the user explicitly mentions a seed dataset)
    # Seed data must live in the Files service or on HuggingFace — see
    # references/seed-datasets.md for the supported sources.
    # config_builder.with_seed_dataset(
    #     FilesetFileSeedSource(path="default/my-seed-data#records.parquet")
    # )

    # config_builder.add_column(...)
    # config_builder.add_processor(...)

    return config_builder

Only include Pydantic models, custom generators, seed datasets, and extra dependencies when the task requires them.