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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

Understanding Large Language Models

Sections

1.1 What is Artificial Intelligence? A Brief Overview
1.2 Introducing Natural Language Processing (NLP)
1.3 Defining Large Language Models (LLMs)
1.4 How LLMs Learn from Text Data
1.5 Examples of Tasks LLMs Can Perform
1.6 Common Misconceptions about LLMs

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