Generative AI Studio

Instructor: Wu-chang Feng
Class: FAB 88-10, M 5:30pm-8:50pm
Office hours: 1pm-3pm, FAB 120-14
Contact and discussion: TA: Ian Kariniemi (ilk@pdx.edu)
Resources
Course Description
An introduction to the reliable, secure, and ethical use of Generative Artificial Intelligence (AI). Students will leverage Generative AI to automate content creation, content consumption, application generation and task execution. Students will learn strategies for prompting models, techniques for providing context to them, security and privacy issues that are associated with them, and the construction of applications powered by them. Students will leverage AI services to build context-aware conversational chatbots, web applications, learning assistants, and workflow automation agents. No prior experience is required.

Schedule

Week Topic Assignments Due (Monday before class)
1 Overview, Models, Prompts, Prompt engineering, Bias issues
 1
2 Context, Context engineering, Retrieval Augmented Generation, Privacy issues 2 Wk01
3 Conversational chatbots, Safety issues 3 Wk02
4 Agents and Tools, Security issues 4 Wk03
5 Agent skills, Personal Assistants 5 Wk04
6 Coding agents, Reliability issues 6 Wk05
7 Multimedia generation, Integrity issues 7 Wk06
8 Learning 8 Wk07
9 Teaching 9 Wk08
10 Decision-making 10 Wk09
Finals Final project Final project (Thursday @11:59pm)

Assignments

Homework
Each week, you will utilize the techniques learned in class to experiment with projects of your own that applies Generative AI towards solving a problem or building an application. You will then perform a 5-minute screencast walkthrough of it and publish it to MediaSpace as an unlisted video. You will then submit the URL in Canvas.
Final project
Based on the course content, a final project will be assigned for you to build a more substantial application that applies LLMs and generative AI towards solving problems of your choosing in a discipline of your choosing.
Late work
Always turn in what you have done on-time. Late work will be docked 20%, but may be turned in at any time before finals week. If you submit any late work, it will be graded during finals week.

Course objectives

  • Evaluate prompt and context engineering strategies to effectively utilize Generative AI
  • Reliably and ethically apply Generative AI to build applications
  • Analyze bias, safety and security issues with Generative AI

Policies

Grading
Attendance and participation 40%
Homework 40%
Final project 20%
Attendance and participation
Attendance is required and participation is mandatory. Participation in the Slack channel is encouraged. You are expected to follow this code of conduct when communicating.
Academic misconduct
  • Includes allowing another student to copy your work unless specifically allowed by the instructor.
  • Includes copying content from external sources without proper attribution
  • Results in a grade of 0 for the assignment or exam.
  • Results in the initiation of disciplinary action at the university level.