docs: some documentation cleanup (#52)

* some documentation cleanup

* typo

Author: johnnygreco

CONTRIBUTING.md

@@ -27,7 +27,7 @@ Whether you're new to the project or ready to dive in, the resources below will
 
 2. **[AGENTS.md](https://github.com/NVIDIA-NeMo/DataDesigner/blob/main/AGENTS.md)** – context and instructions to help AI coding agents work on Data Designer (it's also useful for human developers!)
 
-3. **[Documentation](https://github.com/NVIDIA-NeMo/DataDesigner/blob/main/docs/)** – detailed documentation on Data Designer's capabilities and usage
+3. **[Documentation](https://nvidia-nemo.github.io/DataDesigner/)** – detailed documentation on Data Designer's capabilities and usage
 
 ## Ways to Contribute
 
@@ -40,7 +40,7 @@ Found a bug? Before reporting, please
 2. Search for duplicates in the [issue tracker](https://github.com/NVIDIA-NeMo/DataDesigner/issues)
 
 When [creating a bug report](https://github.com/NVIDIA-NeMo/DataDesigner/issues/new), please include:
-- Data Designer version: `python -c "import data_designer; print(data_designer.__version__)"`
+- Data Designer version
 - Python version and operating system
 - Minimal reproducible example
 - Expected vs. actual behavior

docs/concepts/persons.md

@@ -194,7 +194,7 @@ Each personality trait contains:
 
 ## Person Sampling with Faker
 
-If you do not have access to Data Designer's managed Nemotron-Personas datasets or you need locale that is not covered, Data Designer provides a Faker-based person sampler (`sampler_type="person_from_faker"`) that uses the [Faker library](https://faker.readthedocs.io/en/stable/) to generate person data.
+If you do not have access to Data Designer's managed Nemotron-Personas datasets or you need a locale that is not covered by Nemotron-Personas, Data Designer provides a Faker-based person sampler (`sampler_type="person_from_faker"`) that uses the [Faker library](https://faker.readthedocs.io/en/stable/) to generate person data.
 
 **Important:** This sampler generates random personal details that are **not grounded in real-world demographic data**. It's best suited for testing, prototyping, or when you need basic person attributes in locales not yet covered by Nemotron-Personas.
 

docs/index.md

@@ -25,19 +25,20 @@ Data Designer helps you create datasets through an intuitive, **iterative** proc
 1.  **⚙️ Configure** your model settings
     - Bring your own OpenAI-compatible model providers and models
     - Or use the default model providers and models to get started quickly
-    - Learn more by reading the [model configuration docs](does-not-exist.md)
+    - Learn more by reading the [model docs](models/default-model-settings.md)
 2.  **🏗️ Design** your dataset
     - Iteratively design your dataset, column by column
     - Leverage tools like statistical samplers and LLMs to generate a variety of data types
-    - Learn more by reading the [column docs](concepts/columns.md) and checking out the [tutorial notebooks](notebooks/1-the-basics.ipynb)
+    - Learn more by reading the [column docs](concepts/columns.md)
+
 3.  **🔁 Preview** your results and iterate
     - Generate a preview dataset stored in memory for fast iteration
     - Inspect sample records and analysis results to refine your configuration
-    - Try for yourself by running the [tutorial notebooks](notebooks/1-the-basics.ipynb)
+    - Try for yourself by running the [tutorial notebooks](notebooks/intro.md)
 4.  **🖼️ Create** your dataset
     - Generate your full dataset and save results to disk
     - Access the generated dataset and associated artifacts for downstream use
-    - Give it a try by running the [tutorial notebooks](notebooks/2-create-your-dataset.ipynb)!
+    - Give it a try by running the [tutorial notebooks](notebooks/intro.md)
 
 ## Library and Microservice
 

mkdocs.yml

@@ -12,16 +12,16 @@ nav:
       - Validators: concepts/validators.md
       - Persons: concepts/persons.md
       # - Plugins: concepts/plugins.md
-  - Tutorials:
-      - Overview: notebooks/intro.md
-      - The Basics: notebooks/1-the-basics.ipynb
-      - Structured Outputs and Jinja Expressions: notebooks/2-structured-outputs-and-jinja-expressions.ipynb
-      - Seeding with an External Dataset: notebooks/3-seeding-with-a-dataset.ipynb
   - Models:
       - Default Model Settings: models/default-model-settings.md
       - Configure with the CLI: models/configure-model-settings-with-the-cli.md
       - Model Providers: models/model-providers.md
       - Model Configs: models/model-configs.md
+  - Tutorials:
+      - Overview: notebooks/intro.md
+      - The Basics: notebooks/1-the-basics.ipynb
+      - Structured Outputs and Jinja Expressions: notebooks/2-structured-outputs-and-jinja-expressions.ipynb
+      - Seeding with an External Dataset: notebooks/3-seeding-with-a-dataset.ipynb
   - Code Reference:
       - column_configs: code_reference/column_configs.md
       - config_builder: code_reference/config_builder.md