Concepts and techniques of GenAI for business-oriented applications, including prompt engineering, agents, retrieval augmented generation, foundation model fine-tuning and pretraining, and multimodal models. Responsible GenAI topics, such as copyright infringement, toxicity, bias, and hallucination are also covered. The emphasis is on the design and implementation of basic business-oriented GenAI applications.
Athena Title
Generative AI
Prerequisite
MIST 4600 or MIST 4600E or MIST 4605
Grading System
A - F (Traditional)
Student Learning Outcomes
Students will understand how GenAI could support decision-making in business contexts.
Students will understand the challenges of GenAI in business applications.
Students will understand the GenAI architecture, ecosystem, and orchestration
Students will understand the basic concepts of GenAI technologies, including but not limited to prompt engineering, agent AI, retrieval augmented generation (RAG), Large Language Model (LLM) pretraining, fine-tuning, and Multimodal LLMs.
Students will be able to apply basic prompting techniques in business decision-making.
Students will be able to design and implement basic LLM agents for business applications.
Students will understand the technical strategies to adapt GenAI for domain-specific business problems using RAG, finetuning, and agents.
Students will understand the key concepts of responsible GenAI, including but not limited to safety, security, privacy, copyright infringement, toxicity, and bias.
