Concepts and techniques of Generative AI (GenAI) for business-oriented applications, including prompt engineering, agents, retrieval augmented generation, foundation model fine-tuning and pretraining, and multimodal models. Responsible GenAI topics, like copyright infringement, toxicity, bias, and hallucinations are covered. The emphasis is on the design and implementation of basic business-oriented GenAI applications.
Athena Title
Generative AI
Non-Traditional Format
The course will be taught 95% or more online.
Semester Course Offered
Offered fall
Grading System
A - F (Traditional)
Student learning Outcomes
Students will explain how Generative AI supports business decision-making and identify its key limitations in organizational contexts.
Students will describe the architecture, ecosystem, and orchestration of modern GenAI systems.
Students will differentiate among core GenAI techniques including prompting, agent-based systems, retrieval-augmented generation (RAG), pretraining, fine-tuning, and multimodal models.
Students will apply prompt engineering techniques to support business decision-making tasks.
Students will develop simple LLM-based agents for defined business use cases.
Students will evaluate technical adaptation strategies such as RAG, fine-tuning, and agents for domain-specific business problems.
Students will assess responsible AI considerations including safety, privacy, bias, security, and intellectual property risks in business deployments of GenAI.
