# LLM Labs Introduction

Welcome to Datasaur’s LLM Labs! This guide will introduce you to the key features and tools available in our LLM Labs, designed to enhance your projects. Whether you're a new user or looking to explore more advanced functionalities, this documentation will help you get started. Datasaur offers a **free tier** for new users in a trial period. When you sign up, you will receive a credit to try out our powerful LLM Labs. This allows you to explore various features and assess how they can benefit your projects without any initial cost. In LLM Labs, all activities are organized within a workspace. The workspace is where you perform tasks related to model evaluation, dataset management, knowledge base creation, and model catalog exploration. It supports a collaborative environment, allowing you to invite team members to work together seamlessly. This collaborative setup ensures that all project-related activities and resources are easily accessible and manageable in one place. ### Key features Datasaur LLM Labs empowers you with: * [**Sandbox**](https://docs.datasaur.ai/llm-projects/sandbox)**:** Experiment and interact with various LLMs directly within Datasaur. * [**Knowledge base**](https://docs.datasaur.ai/llm-projects/vector-store)**:** Store and query text embeddings for tasks like semantic search and retrieval augmented generation (RAG). * [**Evaluation (Rating and Ranking)**](https://docs.datasaur.ai/llm-projects/evaluation)**:** Evaluate LLM outputs through human feedback by rating and ranking responses based on quality, accuracy, etc. * [**Automated Evaluation**](https://docs.datasaur.ai/llm-projects/evaluation/automated-evaluation)**:** Set up automatic scoring of LLM outputs against predefined metrics, saving you time and effort in evaluation. ### **Still Have Questions?** If you're still unsure about how to use LLM Labs or have any questions, please don't hesitate to reach out to our support team at . We're here to help you get the most out of our platform and achieve your LLM development goals. --- # 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.datasaur.ai/llm-projects/llm-introduction.md?ask= ``` 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.