As part of our effort at the UConn Digital Frontiers Initiative, we offer an intensive one-day Executive Workshop on Generative AI for business leaders, managers and data scientists. Using tools provided in class, we cover the following course content. Basic knowledge of machine learning and Python programming is recommended, but not required. You do not need to be a programmer to fully benefit from this workshop.

1. Introduction to Generative AI and LLMs
   • Fundamentals of generative AI and Large Language Models (LLMs)
   • The architecture (transformers) and training methods of LLMs
   • Business cases of LLMs and useful tools
2. Effective Prompt Engineering (PE)
   • PE principles and advanced techniques
   • Security attacks and defense
   • Prompt engineering hands-on
3. Retrieval Augmented Generation (RAG)
   • RAG pipeline and practice
   • Text embedding and vector database
   • RAG hands-on
4. Tools, Workflow and Fine-Tuning
   • Tools/Workflow/Agents and hands-on
   • Fine-tuning and computational demands
   • Fine-tuning demonstration
5. Safety, Ethics, and Transformative Potential
   • Safety and ethics issues in LLM applications
   • Explore PE, RAG, and fine-tuning for risk management
   • The transformative potential of generative AI

The workshop is based on some of the key learnings from preparing my master class for the MS BAPM and FinTech programs (more info below). It typically takes the format of one-day training of 6 hours, but can be customized to the length and format most suited for your needs. For workshop related inquiries, contact us here, or just drop me an email.

OPIM 5894: Generative AI for Business is a graduate course designed to equip students with the knowledge and skills needed to leverage generative AI (GenAI) and Large Language Models (LLMs) in business contexts. It will be offered in Fall 2024.

Course Description

This course will delve into the core mechanisms of GenAI, focusing on Large Language Models (LLMs). Participants will explore advanced techniques in prompt engineering, retrieval augmented generation (RAG), fine-tuning LLMs, and ensuring the safety and ethical use of GenAI. The curriculum balances theoretical knowledge with hands-on exercises, enabling participants to apply GenAI innovations to real-world business challenges effectively. By demystifying the complexities of LLMs, this course aims to empower students to leverage these powerful models for creating innovative solutions and navigating the dynamic landscape of generative AI with confidence. By the end of the course, you should be able to:

• Articulate the essentials of LLM architecture, training methodologies, and business applications.
• Implement effective prompt engineering strategies to develop robust business applications using LLMs.
• Integrate RAG into LLM applications to provide coherent and contextually relevant responses.
• Customize LLMs using fine-tuning, balancing model capabilities and computational demands.
• Address safety and ethics issues in LLM applications.
• Engage in thoughtful discussions on the transformative potential of GenAI, recognizing both the opportunities and challenges it introduces.

This course does require that you know machine learning (e.g., from our predictive modeling class) and Python programming. We will delve into the details of fine-tuning in the second half of the semester. So you should be comfortable with Python programs in Google Colab. But in general, this is not a coding heavy class. Instead, the focus is on creating business applications utilizing the rich ecosystem that has been built on top of LLMs.

University of Connecticut, 2023 - present

• OPIM 5894: Generative AI for Business
  • Fall 2024: 38 MS students
• OPIM 3505: Business Database Management
  • Fall 2023: 18 undergraduate students, rating 5/5
  • Fall 2024: undergraduate students
• OPIM 5272: Data Management and Business Process Modeling
  • Fall 2023: 22 MS students, rating 5/5

University of Arizona, 2015 - 2023 (1,034 students)

• MIS 611C: Economics of Information Systems
  • Fall 2020 & 2022: 21 PhD students
  • Avg. rating: 4.96/5
• MIS 543 Online: Business Data Communications & Networking
  • Fall 2019 - Spring 2023: 263 MS students (online, Cybersecurity and MIS)
  • Avg. rating: 4.7/5
• MIS 543: Business Data Communications & Networking
  • Fall 2016 - Fall 2022: 464 MS students
  • Avg. rating: 4.6/5
• MIS 307: Business Data Communications
  • Fall 2015 - Fall 2018: 286 undergraduate students
  • Avg. rating: 4.5/5

UC San Diego, 2013 (32 students)

• MGT 103: Product Marketing and Management
  • Summer 2013: 32 undergraduate students
  • Avg. rating: 4.2/5