Module 4 Overview: Large Language Modules

  • AI
  • AI Literacy

How can understanding Large Language Models (LLMs) help us create and use information wisely?

With the expansion of LLMs across industries, understanding how they work can help us be informed users, creators, and citizens. Throughout Module 4 you will learn how these models are created and what limitations they have based on their development. This material builds on what you have learned about AI in the previous three modules.

By the end of this module, you will be able to:

  • Explain the architecture underlying LLMs
  • Describe the process of training LLMs
  • Distinguish between the different types of bias in LLMs

Content Preview

Below you'll find the topics you'll cover in this module, helping you build a clear understanding of the key ideas. After that, you'll see the activities you'll complete, giving you hands-on practice to apply what you've learned.

Topics

In this module, you'll explore these topics:

  • Why does context matter for LLMs?

  • What are LLMs?

  • How are LLMs Trained?

  • What bias exists in LLMs?

Activities

You'll apply your learning through these activities:

  • ASSIGNMENT: Prompt an LLM with varying context.
  • ASSIGNMENT: Tokenize an input with ChatGPT tools.
  • DISCUSSION: Does human input improve the model’s output or introduce new bias?
  • ASSIGNMENT: Describe LLMs to your colleagues.
  • QUIZ: Review vocabulary and knowledge about LLMs.