Large Language Models (LLMs) are transforming the technology landscape within higher education – allowing institutions to automate complex tasks, extract insights from vast amounts of data, and interact with users through natural language interfaces. The Office of Information Technology has partnered with the academic community in an initiative that educates IT professionals and business citizen developers on how to effectively use LLMs in enterprise settings.

Dr. Polo Chau, an associate professor in the School of Computational Science and Engineering at Georgia Tech and associate director of the Master of Science Analytics program, and Didier Contis, executive director of Academic Technology, Innovation, and Research Computing within OIT, have embarked on an ambitious project to create a short, asynchronous course designed to equip IT professionals and business citizen developers with the knowledge to apply LLMs in enterprise environments.

The course will initially provide a focused, online tutorial that covers LLMs, including advanced AI techniques such as fine-tuning processes. A future version of the tutorial will focus on Retrieval Augmented Generation (RAG) and fine-tuning processes. RAG enables language models to dynamically retrieve external data, improving response accuracy, while fine-tuning allows for the customization of these models to meet specific business needs, from unique vocabularies to specialized tasks.

Two graduate students from Chau’s research group are developing the curriculum to offer hands-on learning experiences. The course will demonstrate the practical application of LLMs, giving learners the tools to innovate and automate within their industries.

The development of this course is timely, given the growing demand for AI capabilities in business environments. It addresses the critical need for skills in managing and deploying LLMs, potentially leading to increased efficiency and the creation of sophisticated user experiences.

This educational initiative is expected to play a crucial role in democratizing AI, making advanced AI techniques accessible to a wider audience beyond AI researchers and data scientists. In doing so, Chau and Contis are not only contributing to the professional development of individuals but also fostering the growth of an ecosystem that embraces technological progress and innovation.

The result of their efforts is a future in which AI integration is not a complex challenge, but a standard practice.