AINoon Tutorial 2

The goal of this tutorial is to make your own chatbot to answer questions based on information in a document.

We’re going to make a chatbot that can answer questions about TechNoon courses, like AINoon.

Create a chatbot

  1. Open chatbase.co
  2. Go to the Dashboard
  3. Click New AI agent
  4. If it forces you to upload a source:
  5. Select Create agent

Playground and settings

What is TechNoon?

It will only give a good answer if you provided the Text source above.

Now let’s look at the options we have to configure our chatbot:

Providing more knowledge

What if we ask it for information about TechNoon it doesn’t have the answer for?

List the available TechNoon courses

Let’s give it more detailed knowledge!

  1. In a new tab, open technoon.org/ainoon
  2. Download the technoon_knowledge.txt
  3. Open the file and have a quick read
  4. From Chatbase, open Sources -> Files from the sidebar
  5. Click the big Upload button and select the txt file.
  6. After uploading, click Retrain agent
  7. After it’s finished “training”, go back to the Playground
  8. Ask it the question again:
List the available TechNoon courses

But how is it using the file?

  1. Every time a question is asked, it searches through the file for chunks that are relevant to the question
  2. Those chunks are added to the prompt so that the LLM can use them to answer the question!

This is a technique called Retrieval Augmented Generation or RAG which we’ll talk more about next week.

Deploying your chatbot

Let’s share our chatbot with the world!

  1. First, let’s give the chatbot a better name

    1. Open Settings -> General from the sidebar
    2. Change the name to TechNoon Bot
      • We should make it clear to our users that this isn’t a human they’re chatting with.
  2. Open Deploy from the sidebar

  3. Select Help Page

  4. Select Deploy

  5. Open the Domain setup tab, and click Visit Page

  6. Try asking a question!

    What can you help me with?
  7. You can now share the link to this page with anyone in the world!

Testing

It’s important to test chatbots for their intended use case.

Refer to the Testing a Chatbot slide

Conclusion

You’ve successfully built a chatbot that can answer questions about your own documents!

This is a great example of a pattern for using LLMs to build apps that extend their capabilities.

Next week we’re going to look more at Retrieval Augmented Generation and other patterns for building applications with LLMs.