Generative AI in Practice Workshop

First principles of AI

• Conceptual understanding of how generative AI and large language models work

• Current developments in applications and extensions

• Economic and organizational principles of AI

• Principles of working with generative AI

• The human–AI task tensor

Interacting with LLMs and generating content

• Introduction to different models

• Obtaining structured outputs

• Using AI to assist with idea generation and refinement

• Tradeoffs in using AI as a substitute versus a complement

Performing data tasks and analytics

• Dataset descriptions

• Data forensics

• Summary statistics and visualizations

• Regression

• Prediction models

• The capability–audit requirement tradeoff

From idea to prototypes

• New product idea development

• From idea to conceptual model 

• Sketch-to-functional model cycle

• Storyboarding

• Marketing collateral

• Business plan development

Creation of solutions and applications

• Python scripts

• Vibe coding

• Functional application development

Use AI to develop new skills

• Curriculum planning

• Interactive tutor

• Learing strategies (e.g., Socratic method, multiple perspectives, error diagnosis, assessments)

• Meta-learning support

Getting AI to take independent action

• Defining agents

• "Agent-lite" approaches

• Off-the-shelf agents

• Workflow automation

• Personal assistant / agent

• Build a manual multi-agent system

• Agent architectures

• Multi-agent system experimentation

• Comparison of multi-agent systems to frontier models