> For the complete documentation index, see [llms.txt](https://context-builder.gitbook.io/helpdocument/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://context-builder.gitbook.io/helpdocument/welcome-to-context-builder/quickstart-guide.md).

# Quickstart Guide

To start using the context builder, let's build a simple application. In this example, we will:

* Add a data source in the data settings.
* Create and refine a Conversational Retrieval QA to answer questions.
* Debug the application, have a conversation with AI, and review the returned results. 

Let's go step by step:

## #Create a new Dataset

After creating a data source, you will see an interface with 2 main configurations: 

Dataset name: Give your data source a name that will be used for future references. 

Document loaders: We allow you to seamlessly import data from any source. Our data loaders perform the following operations:

* Load data from the source.
* Convert data into text or arrays.
* Split the data into smaller segments (with content overlapping).
* Return a list of data segments.

<figure><img src="/files/Od7N29rHCOvCNlvtrsjF" alt=""><figcaption></figcaption></figure>

## #Create a new application

Let's start by creating a new application in "my app." You will see an interface with 3 pieces of information:&#x20;

* App Name: Give your application a name.&#x20;
* Short description: Provide a brief description of this application's features.&#x20;
* Image: Application icon or avatar.

<figure><img src="/files/9DnhDFCCC3FWKg7EvJth" alt=""><figcaption></figcaption></figure>

## #Explore the workflow interface

The "Add task" list provides two options:&#x20;

Tool: Offers pre-packaged, ready-to-use large model tools of different types.

First, let's add a tool type called Conversational Retrieval QA. All configuration options will be displayed in the right drawer. Only two settings are required to complete the process.

* In the "prompt" module, enter the model prompt.
* In the "data" module, add the data source you uploaded earlier.

<figure><img src="/files/guJDygF38KgEf6nF4UW6" alt=""><figcaption></figcaption></figure>

<figure><img src="/files/Kgl6vWzQ2gk8dqNu0Wcu" alt=""><figcaption></figcaption></figure>

## #Debugging the app

Now that our workflow is configured, click the "enter debug" button to access the debugging interface. You can try having a conversation with it, asking some questions related to the data source, and see how it responds.

<figure><img src="/files/1xaA6gB5hBs5sRVrioBP" alt=""><figcaption></figcaption></figure>

## #What's next?

Now that we have built an app ready for production, we can continue maintaining and improving it. Some next steps to try are:

1. Increase debugging: Test your app with multiple inputs to evaluate its intelligence on a larger scale.
2. Share: Share the app with users who need it and gather their feedback.
3. Fine-tuning: Use the data collected from users to fine-tune your custom app and optimize its performance for your specific tasks.