Create a Custom AI ChatBot using your own data

ByJorge Pereira
Tags: , ,

Part of: AI Learning Series Here

Using AI can save tons of time. Increases your productivity exponentially. However public AI Bots tools do not allow you to use your own data at scale, being limited to uploading a single document (typically a limited sized txt or PDF)

If you are looking to take advantage of your own data to help users chat / interact in a natural language to get answers, then you can create your own chatbot without having to create your own language model (LLM). It is a good way to understand how AI can be customized for you own use case. depending on your need you can do it on a local machine, or in the cloud.

The specific implementation details will depend on the platform and tools you choose. But those are the high-level steps to get started building a chatbot tailored to your own data and use case. At a high level, here are the key steps to create a chatbot with your own data:

  1. Collect and prepare your data: Gather the conversational data, information, and knowledge you want your chatbot to have access to. This could include customer service logs, FAQs, product descriptions, etc.
  2. Choose a chatbot platform or framework: There are many options like Dialogflow, Amazon Lex, Microsoft Bot Framework, or you can build a custom chatbot using a framework like Rasa, Botpress, or Chatterbot.
  3. Design your chatbot’s conversational flow: Map out the intents, entities, and dialog flows your chatbot will need to handle user queries effectively.
  4. Train your chatbot’s natural language processing: Feed your data into the platform/framework so it can learn to understand and respond to different user inputs.
  5. Test and refine iteratively: Continuously monitor your chatbot’s performance, gather feedback, and make improvements to its language understanding and responses.
  6. Integrate / Publish your chatbot: Connect your chatbot to communication channels like web chat, messaging apps, voice assistants, etc. so users can interact with it.
  7. Monitor usage / bots and value being provided to users and the organization:

The steps are fairly straight forward:

  1. Choose a Chatbot platform
  2. Choose the base core LLM you want to use
    • you can use the OpenAI , Mistral, Claud or any other out there.
    • Make sure you select one that can handle the questions you want to anseer ( Ir DO not use a text only LLM for Photo identification)
  3. Add your own data source ( Pick or create a location with your source files)
  4. Once uploaded you will need to “index” your documents.
    • (Please note that your data is NOT going back to the LLM (Model). You are not training that model, you are using that model to index your data!)
  5. Fine Tune the model which is basically adjusting the tone, limit responses, and things like that.
  6. Create the end-user front-end
    • You can use HTML/WebApp or integrate into your own application using API calls.

There are fairly good tools that do this for you already, but in this document I am focusing on explaining some of the basics instead.

For a list of tools: go to this page: ChatBot Frameworks and Tools

Best explanation videos I have seen on this topic are:

Fine Tuning:

In machine learning, fine-tuning is the process of optimizing a model for a specific dataset. This can be done by adjusting the model’s parameters, such as the learning rate or the number of hidden layers. 

A few examples from YouTube:

BUilding using tools:

Accomplished Information Technology services professional with over 25 years of experience performing technical pre-sales, solution selling, public speaking / presentations, consulting, project delivery and program management. Over the past few years, Jorge has focused on customers solutions in the areas of: Artificial Intelligence, Generative AI, Workforce Transformation, End-User-Computing (EUC) Lifecycle Management, Modern Device Management, and Cybersecurity.
Additionally, Jorge is also an artist known for his whimsical doodle art and captivating fantasy stories. More on his creative side here

Disclaimer:  I work for Dell Technology Services as a Workforce Transformation Solutions Principal.    It is my passion to help guide organizations through the current technology transition specifically as it relates to Workforce Transformation.  Visit Dell Technologies site for more information.  Opinions are my own and not the views of my employer.

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