Introduction to Large Language Models
Chapter 1: Understanding Large Language Models
What is Artificial Intelligence? A Brief Overview
Introducing Natural Language Processing (NLP)
Defining Large Language Models (LLMs)
How LLMs Learn from Text Data
Examples of Tasks LLMs Can Perform
Common Misconceptions about LLMs
Quiz for Chapter 1
Chapter 2: The Mechanics of LLMs (Simplified)
Representing Words: Tokens and Embeddings
Predicting the Next Word: The Core Idea
The Role of Training Data Size
Understanding Model Parameters
Introduction to Transformer Architecture (High-Level)
How Context Influences Generation
Quiz for Chapter 2
Chapter 3: Communicating with LLMs: Prompts
What is a Prompt?
Basic Prompting Techniques
Providing Instructions Clearly
Giving Examples (Few-Shot Prompting)
Controlling Output Length and Format
Common Prompting Challenges
Practice: Crafting Your First Prompts
Quiz for Chapter 3
Chapter 4: A Look at Different LLMs
Overview of Foundational Models
General Purpose vs. Specialized Models
Open vs. Closed Models: What's the Difference?
Understanding Model Size and Capabilities
Accessing Models: APIs and Interfaces
Quiz for Chapter 4
Chapter 5: Using Pre-trained LLMs
What are Pre-trained Models?
Finding and Choosing an LLM Service
Interacting via Web Interfaces
Introduction to Using LLM APIs
Sending Your First API Request
Interpreting LLM Responses
Hands-on Practical: Simple Text Generation Task
Quiz for Chapter 5
Predicting the Next Word: The Core Idea
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- Attention Is All You Need, Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin, 2017 Advances in Neural Information Processing Systems (NIPS 2017), Vol. 30 (Curran Associates, Inc.) DOI: 10.5555/3295222.3295349 - The foundational paper introducing the Transformer architecture, which underpins most modern LLMs and enables their sophisticated contextual understanding and next-token prediction capabilities.
- Speech and Language Processing (3rd ed. draft), Daniel Jurafsky and James H. Martin, 2025 - A comprehensive textbook covering language models, sequence prediction, and the statistical foundations of natural language processing, which are central to understanding LLM mechanisms. Chapter 3 ("N-gram Language Models") and relevant sections on deep learning for NLP are particularly relevant.
- The Hugging Face Course: How do Large Language Models work?, Hugging Face, 2023 (Hugging Face) - Provides an accessible yet detailed explanation of how large language models generate text through sequential token prediction, probability distributions, and decoding strategies like greedy decoding.