Large Language Models for Business is an introductory course that focuses on the foundations, architecture, training, and corporate applications of modern Large Language Models (LLMs). The course aims to equip students with the necessary knowledge to understand how systems such as ChatGPT or Claude work, why they have become strategically important for companies, and how they can be applied in business environments.
The course covers a range of topics related to modern AI systems, with a primary focus on transformer architectures, training processes, inference, fine-tuning, and Retrieval-Augmented Generation (RAG). Students will receive comprehensive training in the fundamental components of LLMs, including tokenization, embeddings, attention mechanisms, multi-head attention, feed-forward networks, and context windows. In addition, the course emphasizes practical applications of these concepts through coding exercises, model demonstrations, and enterprise use cases.
A key aspect of the course is the careful interpretation of the trade-offs associated with building and deploying AI systems. Students will develop the skills to understand the implications of model size, training costs, inference latency, GPU memory constraints, and retrieval pipelines for business and strategic decision-making. By emphasizing the practical significance of these technical concepts, students will be prepared to evaluate how AI can create value, generate competitive advantage, and support innovation within organizations.
Overall, Large Language Models for Business provides students with a solid foundation in the technical principles and business applications of LLMs, enabling them to understand, evaluate, and apply these technologies in real-world corporate settings. Through this course, students will gain the necessary skills to engage with AI-related opportunities and challenges across a wide range of industries.