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
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
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
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
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
The Mechanics of LLMs (Simplified)
Having established a definition of Large Language Models, this chapter examines their internal workings at a high level. We will look at how these models process and generate text.
You will learn about:
- How text is broken down into units called tokens and represented numerically through embeddings.
- The fundamental concept of predicting the next word as the basis for text generation.
- The significance of training data size and model parameters (P) in determining an LLM's capabilities.
- A simplified overview of the Transformer architecture, a common structure for these models.
- How the preceding text, or context, influences the model's output.
This chapter provides a conceptual foundation for how LLMs operate without requiring deep mathematical or programming knowledge.
Sections
2.1 Representing Words: Tokens and Embeddings
2.2 Predicting the Next Word: The Core Idea
2.3 The Role of Training Data Size
2.4 Understanding Model Parameters
2.5 Introduction to Transformer Architecture (High-Level)
2.6 How Context Influences Generation
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