Instructor : Dr. Nabil Omri, PhD, Data Scientist at Capgemini Engineering

 

Learning Objectives

Learning Objectives

By the end of this workshop, participants will be able to:

  • Understand the concepts and technologies behind generative AI, including LLMs and RAG systems.
  • Design and develop an intelligent assistant using LLMs and RAG for specific business needs.
  • Effectively prepare and index documents.
  • Set up a RAG system that can answer complex questions.
  • Optimize and customize the RAG system to meet specific business needs.

 

Workshop Modalities

Duration: 7h

Number of participants: 20 to 30 people

Location: Tunis

Accessibility: In-person

Language: French

Program

Introduction to Generative AI

  • Definition and fundamental concepts.
  • Use cases of generative AI in business (text generation, creative process automation, etc.). 
  • Introduction to different types of generative AI: text, image, and video generation.

Introduction to Retrieval-Augmented Generation (RAG) Systems

  • What is RAG, and how does it improve text generation? 
  • Combining information retrieval and text generation.
  • Practical applications of RAG in areas such as customer support and document retrieval. 

Practical Work: Create an Assistant Using RAG to Answer Complex Questions from a Specific Database

Introduction to Large Language Models (LLM)

  • Definition and working principles of Large Language Models (LLMs).
  • Comparison of LLMs with other AI models, such as smaller and traditional models.
  • Use cases of LLMs in business: content generation, text analysis, chatbots, etc.

Practical Work: Using an LLM to Generate Textual Content

Integrating RAG and LLM to Create an Intelligent Assistant

  • Benefits of integrating RAG and LLM to create more powerful and efficient intelligent assistants.
  • Developing an intelligent assistant with RAG and LLM
  • Deploying the assistant in a production environment.

Practical Work: Create an Intelligent Assistant Capable of Answering Complex Questions Accurately, Leveraging the Power of LLMs and RAG Techniques.

Teaching Resources

Digital materials for participants (slides, documentation, source code)

Laptops with Internet access

A projector, a whiteboard, or an interactive screen

Application for Workshops
List of workshops
Go to Workshop 3