Day 2 – Introduction to Large Language Models Lesson

Lesson Overview

Large language models, often called LLMs, are the core engines behind many modern AI tools. These models can create text, answer questions, translate content, write computer code, help brainstorm ideas, and much more—all by processing patterns in huge amounts of text data. In this lesson, you’ll learn what makes LLMs unique and why only a handful of companies have the resources to develop them.

Understanding the creation and training of LLMs will help you use them more effectively and with realistic expectations. These models work by taking billions—or even trillions—of words from sources like books, websites, and documentation and then building a knowledge base. This allows them to generate human-like responses by predicting the next word in a sequence.

This lesson is useful if you’re new to LLMs or wondering how tools like ChatGPT and Google Gemini seem so knowledgeable. You’ll hear about practical, everyday ways to use these tools, but also learn about their main challenge—“hallucination,” or confidently making up information. Whether you’re a writer, educator, business owner, developer, or just curious about AI, understanding the basics of LLMs will help you make better use of all the tools built on top of them.

Who This Is For

If you want a simple, practical explanation of what makes large language models tick, this lesson is for you. Consider following along if you are:

• Business professionals interested in using AI for writing, summaries, or brainstorming
• Educators looking to automate or enhance content creation
• Content creators or marketers wanting faster translations or editing help
• Developers who want to use LLMs for code assistance or automation
• Anyone curious about how AI tools like ChatGPT, Gemini, and others work under the hood
  

Where This Fits in a Workflow

Learning about large language models sits at the foundation of using generative AI in your daily tasks. Before you start chatting with tools like ChatGPT or automating workflows, it’s helpful to know what these tools can (and can’t) do, and why. For example, if you’re drafting an email in ChatGPT or asking Google Gemini to summarize a long report, you’re directly using LLM technology. This knowledge also prepares you for future lessons that go deeper—like combining LLMs with other technologies or using them for advanced data analysis.

Building this understanding early helps you spot where LLMs can make your work easier, and where you need to double-check their answers.

Technical & Workflow Benefits

Traditional tasks like drafting emails, translating text, or summarizing reports often required manual work or switching between multiple tools. LLMs simplify this process—they enable you to accomplish these tasks by simply having a conversation or pasting in your text. Unlike older spellcheck or grammar tools, modern LLMs can rewrite, professionalize, shorten, or brainstorm your content in seconds, all while adapting to the tone and detail you prefer.

For example, imagine responding to customer emails: instead of writing each reply from scratch, you can prompt an LLM to generate drafts, saving significant time. Or, if you need a quick translation, you don’t need to visit a separate site—the same tool can handle multiple requests in one place. Compared to previous manual methods, LLMs not only speed up these tasks but also open up new opportunities for creativity, productivity, and collaboration. They also work across a wide range of applications, from writing computer code to summarizing meetings.

Practice Exercise

Choose a recent work or study task that involves writing, summarizing, or brainstorming.

1. Use a tool like ChatGPT or Google Gemini (links in the resources) to help draft a new email, summarize a long document, or generate ideas for a project.
2. Edit the output to match your style or specific needs, and note which suggestions felt most useful.
3. Consider: How did the AI’s response compare to how you would have handled the task manually? Was it faster, more creative, or did you notice any errors where you needed to step in?

Try using LLMs for another routine task and compare the results to your usual approach.

Course Context Recap

This lesson builds your foundational knowledge of large language models—what they are, how they come to exist, and where their strengths and weaknesses lie. Previously, you were introduced to the world of generative AI. Up next, you’ll get hands-on with ChatGPT and explore how to interact with LLMs for specific writing and productivity goals. Continue through the Boot Camp to unlock more practical AI skills and become confident using LLM-based tools in your workflows.