Content

Each week, resources for the in-class exercises will be provided via a Google Drive folder.

Each week, you will also be asked to experiment with Generative AI and report your findings in a Google Slide presentation to the rest of the class.

Slack Account

There is a Slack workspace for Computer Science students here at PSU. If you haven't already, create a Slack account, join the workspace at https://pdx-cs.slack.com and add the course channel linked on the course web page. Course communication will be done through this platform; check the pinned section for important updates.

Google Cloud account

Incognito mode

To avoid applying the coupon you receive to the incorrect account, ensure that these steps are done in an "Incognito" or "Private Browsing" browser window to set up your account.

Then, visit https://console.cloud.google.com and login using your pdx.edu account to enable GCP. If you haven't used GCP yet and do not mind temporarily putting your credit card on the account, apply for the $300 coupon and use it to create a new billing account. Otherwise, visit the coupon redemption URL on the Canvas home page for the course (https://canvas.pdx.edu).

Create Project

Click on the pdx.edu organization from the console.

Then, click on "New Project"

Create a Google Cloud project with your ProjectName from above. You should be taken to your project's Home page. For your lab notebook, you will need to ensure that all of your screenshots for your Google Cloud labs include your ProjectName.

To examine your Billing account and its usage, go to the Billing page from the console at https://console.cloud.google.com/billing

Google Generative AI API

We'll be using Google's Gemini Pro as one of our models as it can be run via the course coupon and does not require students to use a credit card. The model can be easily leveraged by programs running outside of Google Cloud. To do so, we'll first need to enable its API. Navigate to the web console at https://console.cloud.google.com/, then launch Cloud Shell.

Run the following command to enable the API.

gcloud services enable generativelanguage.googleapis.com

API keys that are in the pay-as-you-go tier need to be set up via Google's AI Studio at https://aistudio.google.com. From Google's AI Studio import your cloud project and create an API key using the project. We will utilize this key within AI Studio when building applications that require access to models.

ChatGPT

ChatGPT is OpenAI's Large Language Model service. Visit ChatGPT's site at https://chat.openai.com/. Sign in using your PSU e-mail address.

Claude

Claude is an AI assistant from Anthropic. It allows for free usage, but with limited features. Visit the site at: https://claude.ai/ . Create an account using your school e-mail address.

Gemini

Gemini is Google's multimodal Large Language Model service. Visit Google's Gemini site at https://gemini.google.com/.

NotebookLM

NotebookLM is Google's service that allows users to bring their own data for use with the LLM. Visit the site at https://notebooklm.google/ and access the service.

AI Studio

Google's AI Studio is a general purpose service for building applications using LLMs. Visit the site at https://aistudio.google.com.

Course exercises will require you to take screenshots to add to slide presentations. To begin with, for the computer you're using for the course, prompt a model for instructions on how to capture a part of your screen as an image.

Next, we'll use AI to find out more about it. Prompt a model to find out the top uses for Generative AI. 

What are the top 10 uses for Generative AI

Then, compare the output to this list https://hbr.org/data-visuals/2025/04/top-10-gen-al-use-cases

Finally, prompt a model to find out the different types of models available (e.g. coding, video, image, etc.) and the best ones for each type.

What are the top generative AI models for each type of model (e.g. reasoning,
coding, image, video, audio)?

Then, compare the output to this list https://arena.ai/leaderboard and what has been given in the slides.

Like all forms of communication, communicating with a model using prompts takes practice to become effective at it. In this exercise, you will compare the results of identical prompts given to two different models of your choice over simple and advanced tasks.

Simple tasks

Summarization

Models can take large amounts of text and summarize it. Find the text for a multi-page document or speech. Examples might include:

Then, prompt the models to generate a short summary and compare the outputs

Summarize the text below in 100 words or less:
   <TEXT>

Story generation

Write a 200-word horror story where every sentence starts with the next 
letter of the alphabet (A, B, C...). It must have a coherent plot with a 
twist ending.

Completion

The sentence 'I saw her duck' has multiple meanings. List every possible 
interpretation, provide a context sentence for each, and then write a short
comedy sketch where the ambiguity causes a misunderstanding

Reasoning tasks (link)

Multi-step math reasoning

A snail climbs 3 feet up a well during the day but slips back 2 feet at 
night. The well is 30 feet deep. On what day does the snail reach the top?

Logical reasoning

Five houses in a row are painted different colors. The green house is 
immediately to the right of the ivory house. The person in the middle house
drinks milk. Given only these three clues, what are all the valid
arrangements?

Causal reasoning

A company notices that online sales and website crashes both increase 
during major promotions. A new manager suggests canceling all promotions to
reduce crashes. Write a memo explaining why this is incorrect, describe the
actual relationship between the two trends and propose a real solution.

Prompts can be a doorway to model functionality. It is important that one develops the ability to communicate with a model in a way that maximizes their utility while minimizing the time it takes for you to extract results from it. For this exercise, you will experiment with prompt strategies and examine how the output of an LLM changes as your prompt strategies change.

Begin by prompting an LLM for the 3 most important strategies to remember when developing prompts as well as example prompts showing ineffective versus effective prompts. Then, for each prompt strategy, deliver the prompts to an LLM of your choice and qualitatively compare the differences in output.

Next, you will try out different prompts that have been collected from a variety of sources (link) and augment it with any of the strategies you have learned. For each prompt, examine the output of the original prompt and the modified one.

Five essential questions

Analyze this link <LINK> and generate 5 essential questions that, when 
answered, capture the main points and core meaning.

Reasoning

A store had 24 apples. They sold half of them in the morning and a third of 
the remaining in the afternoon. How many apples are left?

Dialectical conversation

Create a dialectical conversation between two people: Amber & Rick.

The topic they are discussing is: <TOPIC>

Model benchmarking

Models are trained with different capabilities. Selecting the right model for a particular task is a continuous process. For this part of the homework, you will experiment with a variety of tasks and examine how well different LLMs perform them. To do so:

Models and your career

Generative AI is changing the nature of work in many disciplines overnight. In this exercise, for the career you are currently interested in pursuing, use the prompt strategies learned in class to:

Next, as you map out your academic career and prepare for the future, you will need to understand how you might prepare best for it. Using the prompt strategies learned in class:

Screencast

Upon completing your experiments, via a narrated screencast of no longer than 5 minutes, you will perform a demonstration and walk-through of your results. Ensure that the video camera is turned on initially in your screencast. You may use screencast software of your choice. Options include video conferencing applications such as Google Meet and Zoom or dedicated programs such as OBS Screen Recorder, QuickTime (MacOS), Screencast-O-Matic (Windows), or RecordMyDesktop (Linux). In addition, CaptureSpace Lite is available via PSU's Media Space (https://media.pdx.edu).

Rubric

We will be using the following rubric to evaluate your homework.

Instructions followed properly including length of screencast and video camera initially turned on

Demonstration of prompt strategies used in formulation of each prompt

Walkthrough and analysis of the results of each prompt

Submission

Upload your completed screencast on MediaSpace. Ensure that it is published as "Unlisted". To do so, visit MediaSpace and click on "My Media".

Click on the screencast video that has been uploaded. Then, in the tabs below, select the "Publish" tab, click on "Unlisted", and then "Save".

Then, in Canvas, submit the URL that your unlisted screencast on MediaSpace is located.