Introduction To Generative AI

Catalog description

This course introduces students to the foundations and applications of Generative AI, with a focus on understanding and applying large language models (LLMs). Students will learn core concepts such as prompt engineering, transformer, diffusion models overview, tokenizers, and model embeddings. The course also explores retrieval-augmented generation (RAG), including naive, advanced, and graph-based approaches, as well as building AI agents and other special topics in Agentic AI and Generative AI. Prerequisite(s): ITS 14000 FOR LEVEL UG WITH MIN. GRADE OF C Course Learning Outcomes 1. Explain the foundational concepts of Generative AI, including transformers, diffusion models, tokenizers, embeddings, and RAGs. 2. Apply basic prompt engineering techniques to guide large language models (LLMs) effectively. 3. Describe and implement retrieval-augmented generation (RAG) approaches and understand the basics of Agentic AI systems. 4. Build an application using Agents powered by LLMs. Examples of projects: Implementing a board game, developing a master-chef agent. View Class Schedule