Dust.tt tutorial

Creating your own AI assistant may seem like a complex task, especially if you don't have coding experience. Fear not, today we'll walk you through how you can build your own auto-GPT apps without coding. We're going to explore the world of AI and see how you can make use of it, even with limitations, using tools like Laning and Dust TT.

Understanding Auto-GPT and Its Limitations

Auto-GPT (Generative Pretrained Transformer) enables the creation of AI applications that can perform a multitude of tasks, such as creating to-do lists, sending emails, or even searching the web. However, as robust as it is, it has its limitations. For instance, integrating additional functions such as APIs or sending emails directly requires coding knowledge and more extensive setups, which can feel overwhelming for non-programmers.

Embracing the Power of Laning and Dust TT

Laning is an open-source library that helps you build powerful AI apps beyond the usual capabilities of GPT. For instance, it enables the AI to access Google search and import vast volumes of documents like financial reports. However, it requires Python programming skills, which is where Dust TT steps in.

Dust TT is a unique tool that enables you to build and deploy AI apps without any coding experience. It offers a peer-type interface that enables you to piece together different function blocks into a workflow powered by large language models, thus making app development simpler.

Step-by-Step Guide to Building an AI App Using Dust TT

Step 1: Creating an Account

Head over to Dust TT and create an account using GitHub or Google. Enter your OpenAI API key under the provider tab.

Step 2: Creating Your First App

Give your app a title, for instance, "code_email_GPT". This opens up the main interface of Dust TT, which is straightforward to use. It resembles Zapier, where you can stitch together different function blocks to build a workflow.

Step 3: Building Your AI App

Firstly, add an 'Input' block. This will be the starting point of the app, where the user provides necessary information. In our case, it would be the name and profile of the clients.

Secondly, add an 'LLM' (Large Language Model) function block. This allows you to call the Large Language Model and provide a specific prompt and configurations. With these two building blocks, you can achieve the app's functionality.

Step 4: Creating and Defining Inputs

Click 'Create dataset' and define the inputs. In this case, it would be 'name' and 'profile'. Specify the type of data these inputs will be and you can predefine some inputs for testing purposes.

Step 5: Using LLM Block

Add an LLM block to call the large language model, such as GPT-3.5. Insert a prompt to guide the AI in performing its task, such as writing personalized cold emails to your clients.

Step 6: Testing the AI App

Click on the round button to test the results. If it's working correctly, it should return a specific email to the client based on the profile provided.

Step 7: Enhancing Results with Code Block

Add a 'Code' block to run any type of code execution. This will enable you to extract specific information from the results.

Step 8: Fine-tuning AI with Training Data

If you want the AI to mimic your writing style, introduce a new function block called 'Data'. This function allows you to insert training data to fine-tune the AI model for better results.

Step 9: Creating a Web App

Once you have your AI app ready to use, Dust TT also provides an API endpoint for you to build an entire app. With GPT, you can ask for step-by-step instructions, and you can even build a web app with the instructions from GPT.

That's it! You can now build your own auto-GPT apps without any coding. Remember, these are just the most basic use cases you can do in Dust TT. There are many different functionalities you can explore, so continue experimenting and learning from the community.

If you have any questions or want to share the AI apps you want to build, feel free to comment below. Stay tuned for more AI discussions and updates.