Introduction to the Course

Explore the fundamentals of large language models in this introductory lesson. Learn what LLMs are, how they generate text and code, and understand ethical considerations. This lesson helps you build a strong mental model to reason effectively about LLMs and prepare for practical applications in AI.

We'll cover the following...

A glimpse of what’s possible
Intended audience
How this course is structured
Prerequisites and setup

Welcome to the world of large language models! It seems like everywhere you look, terms like “generative AI” and “LLMs” are making headlines. They represent a fundamental shift in how we interact with information and technology. We are moving from a world of writing explicit instructions to one where we can guide intelligent systems that reason, generate, and create.

Understanding what’s happening under the hood is essential for working effectively, responsibly, and creatively with these models.

This course is designed to be your guide. We will understand LLMs by taking a journey from first principles. You will move beyond API calls to:

Build a strong mental model: Understand the intuitive “why” behind core concepts like tokens, embeddings, and attention.
Trace the life of a prompt: Follow a single prompt from input to output, seeing exactly how an LLM “thinks” at each step.
Learn the practical toolkit: Go beyond theory and explore the patterns for working with LLMs, including prompt engineering, retrieval-augmented generation (RAG), and function calling.
Develop critical judgment: Learn when and why to choose one technique over another and understand the ethical considerations that come with this powerful technology.

By the end of this course, you will be able to reason about LLMs, enabling you to approach problems more creatively and effectively.

A glimpse of what’s possible

In the coming lessons, we’ll define a large language model from first principles. But before we dive into the theory, let’s explore what these models can actually do. Their capabilities stem from one core function: recognizing patterns in data and generating statistically plausible outputs.

Seeing these capabilities in action provides a helpful context for the technical journey ahead.

Generating creative text

LLMs excel at generating creative and structured text. By learning from a vast library of books, articles, and poems, they internalize the patterns of rhythm, style, and structure. When given a prompt, they can apply these learned patterns to create new content.

For example, if you ask a model to write a poem about the ocean, it isn’t “feeling” the ocean. It’s assembling a sequence of words that are statistically likely to appear together in a poetic context related to the ocean.

The ocean’s breath, a gentle sigh,

A vast expanse beneath the sky,

Waves that dance with endless grace,

A rhythm time cannot erase.

Writing and explaining code

The same pattern-matching ability extends to programming languages. Since an LLM’s training data includes countless lines of code from sources like GitHub, it learns the syntax, structure, and common patterns of languages such as Python, JavaScript, and others.

This allows it to act as a helpful programming partner, capable of generating functions from a simple description or explaining what a complex block of code does in plain English.

Prompt: “Write a Python function that checks if a given string is a palindrome.”

Python 3.10.4

import re
def is_palindrome(s: str) -> bool:
    """
    Checks if a given string is a palindrome, ignoring case and
    non-alphanumeric characters.
    A palindrome is a word, phrase, or sequence that reads the same backward as
    forward.
    Args:
        s: The input string to check.
    Returns:
        True if the string is a palindrome, False otherwise.
    """
    # Normalize the string: remove non-alphanumeric characters and convert to lowercase.
    # The regex pattern '[^a-zA-Z0-9]' matches any character that is not a
    # letter (a-z, A-Z) or a digit (0-9).
    normalized_s = re.sub(r'[^a-zA-Z0-9]', '', s).lower()
    # Compare the normalized string with its reverse.
    # The slice [::-1] creates a reversed copy of the string.
    return normalized_s == normalized_s[::-1]
# --- Example Usage ---
if __name__ == "__main__":
    test_strings = [
        "racecar",
        "A man, a plan, a canal: Panama",
        "hello world",
        "No 'x' in Nixon",
        "12321",
        "not a palindrome",
        "Was it a car or a cat I saw?"
    ]
    print("Checking for palindromes:")
    for text in test_strings:
        result = "is" if is_palindrome(text) else "is not"
        print(f"'{text}' {result} a palindrome.")

Understanding nuance and humor

Perhaps most surprisingly, LLMs can even generate humorous content, such as jokes. This demonstrates an ability to recognize the subtle patterns of language that signal humor, such as puns, misdirection, and absurdity. A good joke often relies on an unexpected but plausible connection between two ideas, a task well-suited for a powerful pattern-matching system.

Prompt: “Tell me a joke about AI.”

Response: Why did the AI break up with the computer? It said it needed more space.

Interactive demonstration: Explore for yourself

Below is an interactive window where you can interact with a real LLM. Use it as a playground to test the capabilities we just discussed. The goal isn’t just to see the answer, but to observe how it answers.

Try a few prompts to see what you discover:

Test its creativity: Ask it to “Write a haiku about a coffee cup.” Notice the structure it follows.
Test its knowledge: Ask it, “Explain the concept of recursion in a simple way.” Does it use an analogy?
Test its conversational ability: Start with a question, then ask a follow-up question that refers to its previous answer. Does it maintain context?

The responses below are generated in real-time by a large language model. The model may occasionally produce content that is inaccurate, biased, or offensive. These outputs are a direct result of the AI’s training data and do not reflect the views or values of Educative.

You will find this course valuable if you are a:

Software developer looking to integrate LLMs into new or existing applications.
Data scientist or analyst who wants to understand the mechanics of modern language models.
Product manager or technical leader who needs to make informed decisions about AI strategy.
Student or researcher exploring the cutting edge of artificial intelligence.
Curious hobbyist with a Python background who loves to learn how things work

How this course is structured

Our journey is broken down into five distinct modules, each building on the last:

Chapter 1: Welcome to the World of LLMs
We start with the fundamentals, defining what an LLM is and establishing the ethical framework for our work.
Chapter 2: The Inference Journey
We trace a prompt step-by-step through a model to see exactly how it generates a response. This is the core of “how an LLM thinks.”
Chapter 3: The Training Journey
We’ll take a peek behind the curtain to understand conceptually how these massive models are trained and aligned to be helpful.
Chapter 4: Building with LLMs
With our foundation in place, we’ll dive into the practical toolkit for building real-world applications with prompting, RAG, and tools.
Chapter 5: The Road Ahead
We’ll conclude by looking at emerging trends and discussing how you can continue your learning journey in this exciting field.

Prerequisites and setup

The only technical prerequisite is a working knowledge of Python. You do not need any prior experience with machine learning or AI. We expect you to be comfortable with:

Writing functions and using variables.
Working with basic data structures, especially lists and dictionaries.
Installing and importing third-party libraries using a tool like pip.

You do not need a background in advanced mathematics or machine learning frameworks like PyTorch or TensorFlow. Our code explorations will use simple, high-level libraries to illustrate concepts.

Now, let’s get started. Our journey begins with a simple question: What, exactly, is a large language model?

1.Course Overview

2.The Inference Journey

3.The Training Journey

4.Building with LLMs: The Developer’s Toolkit

5.Wrap Up