Getting started

Make sure you are able to log in to https://app.refuel.ai. If you don’t have access yet, sign up here.

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.

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. 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:

For more information on how to upload datasets, see here.

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.

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.

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.

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:

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.