> ## Documentation Index
> Fetch the complete documentation index at: https://docs.refuel.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Getting started

In this tutorial, we'll walk through the steps to get set up with Refuel, and building your first data transformation application.

<Steps>
  <Step title="Get access to Refuel">
    Make sure you are able to log in to [https://app.refuel.ai](https://app.refuel.ai). If you don't have access yet, sign up [here](https://www.refuel.ai/get-started).
  </Step>

  <Step title="Create a new project">
    Once you log into Refuel Cloud, the first thing you'll do is create a project.
    A project is a logical grouping for your datasets, models, and applications.

    <img src="https://mintcdn.com/refuelai/Ro4ZLj7W42QNS-H_/images/projects.png?fit=max&auto=format&n=Ro4ZLj7W42QNS-H_&q=85&s=9d97a7b92062c572a46c3fab9ca4ce88" alt="Create a new project" width="2274" height="1142" data-path="images/projects.png" />
  </Step>

  <Step title="Upload your first dataset">
    In order to build your first application, you'll need to upload a dataset, a collection of rows of structured/semi-structured data that you want to transform with LLMs.

    The easiest way to get started is to use our [sample dataset](https://gist.github.com/nihit/74abe1d926acba2d45c68631f216b096). It is a .csv file consisting of a collection of user reviews of businesses on Yelp.com. In the UI, click on "Add Dataset", and upload the CSV file:

    <img src="https://mintcdn.com/refuelai/Ro4ZLj7W42QNS-H_/images/upload_dataset_step1.png?fit=max&auto=format&n=Ro4ZLj7W42QNS-H_&q=85&s=9d4a761969ad11a37260b55b9e380565" alt="Upload a dataset" width="2328" height="704" data-path="images/upload_dataset_step1.png" />

    For more information on how to upload datasets, see [here](/guides/datasets).
  </Step>

  <Step title="Define your first task">
    Once you've uploaded your dataset, you'll need to define a task. A task is a set of LLM guidelines for the transformation you want to perform on your dataset.

    For example, for the Yelp dataset, you might want to create a task that categorizes the sentiment of the review as positive, negative, or neutral.

    <iframe src="https://www.loom.com/embed/705b79988ead41a6965caee8e50943ae?hideEmbedTopBar=true" frameBorder="0" allowFullScreen className="w-full aspect-video" />

    Tasks can be fairly complex and consist of a chained execution of multiple steps. For more information on how to define and iterate on task definitions, see [here](/guides/tasks).
  </Step>

  <Step title="Improve quality with feedback">
    Once you've defined a task and saved it, Refuel will start generating LLM outputs for your dataset. You can review outputs, and provide feedback to iteratively improve quality.

    <iframe src="https://www.loom.com/embed/e29bdf31eebd4a56bb015180a03821c7?hideEmbedTopBar=true" frameBorder="0" allowFullScreen className="w-full aspect-video" />
  </Step>

  <Step title="Deploy your task as an application">
    Once you've iterated on task guidelines and improved output quality with feedback, you can deploy it as an application. Think of an application as a versioned snapshot of a Task that is deployed behind an API to serve realtime traffic.

    Any deployed application can be used to transform new data in realtime. For example, you can use the sentiment analysis application to transform new Yelp reviews as they come in:

    <img src="https://mintcdn.com/refuelai/Ro4ZLj7W42QNS-H_/images/application_playground.png?fit=max&auto=format&n=Ro4ZLj7W42QNS-H_&q=85&s=68ec4478c0e22d9f399ab064d9e95471" alt="Deploy an application" width="1894" height="1518" data-path="images/application_playground.png" />

    The `Playground` tab is a great way to test out your application, and retrieve the relevant code snippets to integrate it programmatically into your stack.

    For more information on how to deploy and manage applications, see [here](/guides/applications).
  </Step>
</Steps>

That's it! You've now built your first application with Refuel 🎉