Build AI Assistants with Large Language Models


Over the past year, there has been a surge in the popularity of Large Language Models (LLMs). However, how can we effectively leverage LLMs to augment our businesses? One example would be the integration of LLMs into existing business frameworks through the deployment of AI Assistants. These assistants serve as invaluable tools in addressing customer inquiries and minimizing the demand for technical support within organizations. In this session, we will dive into the practicalities of utilizing LLM-powered AI Assistants and seamlessly integrating them into established systems.

This workshop provides an easy-to-follow guide on how to use LLMs, configure the settings for your first AI Assistant with LLMs, and seamlessly integrate AI Assistant into an established system.

Session Outline:

1. Learn about LLM basic

We will be using LLMs hosting on IBM Digital Self-Serve Co-Create Experience (DSCE), but you can also use models that are hosted on other platforms such as Huggingface.

2. Configure the settings for your first AI Assistant with LLMs

Learn the basics of watsonx Assistant and create the first AI conversation with LLMs. Then apply this chatbot to an established system.

Background Knowledge:

The attendees will learn about the concept of building a chatbot, create AI conversation, and integrate it into production.


James Busche is a senior software engineer in the IBM Open Technologies Group, currently focused on the Open Source CodeFlare project. Previously, James has been a DevOps Cloud engineer for IBM Watson and the worldwide Watson Kubernetes deployments.

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