# Getting started with Datasaur

Welcome to Datasaur, a web-based platform for managing data labeling workflows for Data Studio and LLM projects.\
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The platform provides tools to support efficient and accurate labeling across multiple data types. It also includes advanced LLM tools designed to support collaboration, improve workflow efficiency, and help you build higher-quality language models securely.

### Data Studio

Data labeling is a key step in training supervised learning AI models. A labeling workflow typically includes three main components: the data to label, the label set (also known as the ontology or taxonomy), and the people performing the labeling.

Datasaur s a web-based platform for managing data labeling workflows, including importing data, applying labels, and collaborating with your labeling team. The platform is designed to improve labeling efficiency and quality while maintaining data security. The platform also includes AI-powered features to help automate the labeling process.

Datasaur supports common Natural Language Processing (NLP) labeling workflows, including:

* [Span labeling](/data-studio-projects/nlp-task-types/span-based.md)
* [Textual / row classification](/data-studio-projects/nlp-task-types/row-based.md)
* [Document classification](/data-studio-projects/nlp-task-types/document-based.md)
* [OCR labeling](/data-studio-projects/nlp-task-types/span-based/ocr-labeling.md)
* [Bounding box labeling](/data-studio-projects/nlp-task-types/bounding-box.md)
* [Audio labeling](/data-studio-projects/nlp-task-types/span-based/audio-project.md)
* [Conversational labeling](/data-studio-projects/nlp-task-types/conversational.md)

{% hint style="info" %}
To get started with Data Studio, see the [Data Studio documentation](/data-studio-projects/data-studio-introduction.md).
{% endhint %}

### LLM Labs

Building and improving LLMs typically involves several key components: experimenting with prompts and models, managing knowledge and context for retrieval, and evaluating outputs for quality and accuracy.

In LLM Labs, you can experiment with different LLMs, manage knowledge bases for semantic search and RAG workflows, evaluate model outputs through human feedback and automated scoring, and streamline AI development workflows in one place. The platform is designed to improve efficiency, collaboration, and output quality while maintaining data security.

{% hint style="info" %}
To get started with LLM Labs, see the [LLM Labs documentation](/llm-projects/llm-introduction.md).
{% endhint %}

### Workspace management

Effective workspace management is an important part of successful data labeling and LLM development workflows. Datasaur provides tools for managing teams, reviewing work quality, and monitoring project activity through reports and QA workflows.

{% hint style="info" %}
To get started with workspace management, see the [Workspace documentation](/workspace-management/workspace.md).
{% endhint %}

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More questions? Email <support@datasaur.ai>


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