Applied Generative AI for Business

This unit advances students from foundational GenAI concepts into applied business workflows, combining creative content production, multimodal systems, and technical model workstreams. Learners will practice prompt engineering, data preparation, model integration, and responsible-AI evaluation to produce prototype solutions and troubleshoot real-world deployment issues in preparation for the culminating unit.

Learning Objectives

• Analyze ethical, legal, and safety risks in generative AI outputs and produce documented mitigation strategies aligned with responsible AI principles
• Apply prompt-engineering techniques, reward/punishment strategies, and multimodal generation workflows to create and iteratively optimize business-oriented content (news, speech, multimedia) with measurable improvements in quality or engagement
• Demonstrate data preparation, code translation (e.g., MATLAB to Python), and system-integration skills to build and deploy a functional GenAI prototype (e.g., FinBERT tuning, LLM financial advisor, or autonomous agent), including resolving integration and performance issues
• Evaluate generative models and algorithms (including model families, reinforcement approaches, and financial-model applications) using appropriate metrics, reproducible research designs, and citations, and recommend model or workflow improvements for business use cases