# Project and Dataset

- [Create](https://docs.supervisely.com/data-organization/project-dataset/create.md): This article provides a step-by-step guide to creating classes and tags: how to define them in the project before annotation and how to add them directly in the labeling tools.
- [Data Structure](https://docs.supervisely.com/data-organization/project-dataset/data-structure.md): This article explains the Data structure in Supervisely, including how Projects, Datasets, and Files are organized in Team and Workspace. Learn how to navigate, manage, and structure your data.
- [Define Classes & Tags](https://docs.supervisely.com/data-organization/project-dataset/define-classes-tags.md): This article explains how to create and manage classes and tags for data annotation in the Labeling Tool to ensure structured and consistent labeling within a project.
- [Gallery & Table views](https://docs.supervisely.com/data-organization/project-dataset/gallery-table-views.md): This article is about how gallery and table views let you customize data display: gallery provide a quick visual overview, while tables offer detailed, sortable comparisons.
- [Collections](https://docs.supervisely.com/data-organization/project-dataset/collections.md): Collections are custom selections of data within a project. They enable flexible filtering and control over annotation workflows.
- [Project Versions](https://docs.supervisely.com/data-organization/project-dataset/project-versions.md): Learn how to use Project Versions in Supervisely. Save, restore, and track project states, instantly preview data, and visualize data evolution with MLOps Workflow.
- [AI Search](https://docs.supervisely.com/data-organization/project-dataset/ai-search.md): This article is about AI Search, which quickly finds images using semantic similarity powered by CLIP. It supports prompt-based and diverse search modes with automatic embedding updates.
- [Quality Assurance & Statistics](https://docs.supervisely.com/data-organization/project-dataset/quality-assurance-and-statistics.md): Understanding your data's characteristics is a key aspect of data preparation. The Statistics section provides tools for data analysis, calculation of statistical metrics and data visualization.
- [Practical applications of statistics](https://docs.supervisely.com/data-organization/project-dataset/quality-assurance-and-statistics/practical-applications-of-statistics.md): Learn how to use best quality assurance and interactive statistical tools to perfect your custom training datasets and improve neural network performance.
- [Project Settings](https://docs.supervisely.com/data-organization/project-dataset/project-settings.md): Configure project-level settings in Supervisely, including the labeling interface and Read-only mode to protect your data from accidental changes.
- [Advanced](https://docs.supervisely.com/data-organization/project-dataset/advanced.md)
- [Custom Data](https://docs.supervisely.com/data-organization/project-dataset/advanced/custom-data.md): Explore how to store and manage technical metadata, configurations, and integration settings using Custom Data in JSON format.
- [Validation Schemas](https://docs.supervisely.com/data-organization/project-dataset/advanced/validation-schemas.md)


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.supervisely.com/data-organization/project-dataset.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.