Homework 3: Large language model (LLM) prompting

Due 2025-04-14, 11:59pm. Instructions last updated 2025-04-07.

Learning objectives

After completing this assignment, students will be able to:

  • Prompt LLMs programmatically with templates (parameterized)
  • Demonstrate the difference between zero-shot, few-shot, and chain-of-thought prompting
  • Engineer and test different prompts

Overview

In this assignment, you will explore different prompting techniques for OpenAI LLMs. You will fill in a Jupyter notebook hosted on the Pitt CRCD to run your code.

To get started, click on the class nbgitpuller link and edit the template notebook, hw3_template.ipynb. You can run it on the standard CPU server; no GPU is needed.

OpenAI account

The class OpenAI account is available for use in this assignment. Please see Canvas for an announcement with the API key you can use for the class account.

Deliverables

  1. Your code: the Jupyter notebook you modified from the template. Submit:
    • your .ipynb file
    • a .html export of your notebook. To get a .html version, click File > Save and Export Notebook As… > HTML from within JupyterLab.
  2. A PDF report with answers to questions provided in the template notebook. Please name your report hw3_{your pitt email id}.pdf. No need to include @pitt.edu, just use the email ID before that part. For example: report_mmyoder_hw3.pdf. Make sure to include the following additional information:
    • any additional resources, references, or web pages you’ve consulted
    • any person with whom you’ve discussed the assignment and describe the nature of your discussions
    • any generative AI tool used, and how it was used
    • any unresolved issues or problems

Please submit all of this material on Canvas. We will grade your report and may look over your code.

Background readings

The following optional readings are good references for LLM prompting:

Acknowledgments

This assignment is based on a homework assignment designed by Mark Yatskar and provided by Lorraine Li.