Disclaimer: Due to time constraints, this course primarily focuses on lectures with limited hands-on practice opportunities. The full scope of the stated course content may not be delivered in its entirety. Additionally, the tutor and course content are subject to change without prior notice. We appreciate your understanding and flexibility.
#3
AI for Business: Retrieval-Augmented Generation (RAG) for Beginners
Course Objectives
To introduce participants to RAG systems and their practical applications in business contexts.
Course Contents
- Introduction to AI and RAG in Business (20 mins)
- Overview of AI and RAG concepts
- Advantages of RAG in handling large datasets
- RAG Fundamentals (25 mins)
- Key components: retrieval mechanisms and language models
- Data preparation for RAG systems
- Practical Applications of RAG (30 mins)
- Customer support: chatbots and virtual assistants
- Marketing: personalized content generation
- Report generation and data analysis
- Implementing RAG in Business (15 mins)
- Integration with existing business software
- Best practices for data security and privacy
Course Highlights
- Focus on practical applications of RAG in business
- Case studies demonstrating real-world benefits of RAG systems
- Overview of future trends in business AI
Course Tutor Introduction
Prof. Jenny Kwok
Research Assistant Professor in the Faculty of Arts at the University of Hong Kong
Prof. Jenny Kwok is a Research Assistant Professor in the Faculty of Arts at the University of Hong Kong and a Visiting Fellow at Cambridge Digital Humanities, University of Cambridge (2024-25). As the Lab Coordinator of the Arts Tech Lab, she leads research on the application of Large Language Models (LLMs) in computational literary studies, with a strong focus on enhancing the accuracy and interpretive depth of language processing.
Prof. Kwok’s expertise extends into business applications, particularly in fine-tuning Retrieval-Augmented Generation (RAG) models to ensure their reliability in decision-making processes. One of her key research focuses is exploring how RAG technology can be effectively integrated into business settings, ensuring that it meets stringent accuracy standards and complies with due diligence requirements, making it suitable for high-stakes environments.
Solomon Ho
Researcher at the Big Data Studies Lab at the University of Hong Kong
Solomon Ho is a researcher at the Big Data Studies Lab at the University of Hong Kong, focusing his work mainly on computational linguistics and digital humanities. He has consistently shown enthusiasm for leveraging cutting-edge technologies like large language models and computer vision to explore the fields of arts and humanities.