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4 Chapters · 42 Lessons

AI Literacy: Essential Knowledge for Coexisting with AI

Learn the core concepts and key application areas for understanding AI.

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Chapter 1

Essential AI knowledge for coexisting with AI

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Lessons

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How Did AI Gain Intelligence?

In AI, All Information Is Represented as Numbers

Types of AI and Generative AI

What Does AI Learn, and How? The Principles Behind Learning from Data

What Does It Mean to Adjust Weights in AI Training?

The Transformer Model That Popularized Generative AI

Tokens and the Context Window

Inference: The Process of Constructing New Answers from Learned Patterns

How AI Generates Responses Probabilistically

The Concepts of Temperature and Top-p

AIs Mistake: What Is Hallucination?

Why Did GPUs Become So Valuable in the Age of AI?

The Story Behind NVIDIAs Rise and the Concept of CUDA

Googles Counter: The TPU (Tensor Processing Unit)

Chapter 2

Characteristics and usage strategies for major AI models

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Lessons

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The Design Philosophies of GPT, Claude, Gemini, Grok, and DeepSeek

How Much Data and Cost Does It Take to Build a Large Language Model (LLM)?

GPT: The Model That Created the Generative AI Paradigm and Changed the World

Claude: A Conversational AI That Balances Long Context and Safety

Gemini: A Conversational AI That Embraces the Google Ecosystem and Multimodal Processing

Grok: A Direct AI That Reflects Real-Time Information

DeepSeek: An AI Specialized in Efficiency

A Guide to Choosing the Right AI for Your Task

5 Tips for Reducing AI Operating Costs

AI Practice: Comparing Responses from Multiple Models

Chapter 3

Prompt engineering, fine-tuning, and AI utilization strategies

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Lessons

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What Is a Prompt?

How to Write Prompts That Get the Answer You Want on the First Try

How Does AI Interpret Your Questions?

Zero-Shot Prompting: Asking Without Examples

Few-Shot Prompting: Asking with Examples

One Step at a Time: Chain of Thought Prompting

Our AI Has Changed! Fine-Tuning to Build a Custom AI

Fine-Tuning vs. Prompt Engineering: Whats the Difference?

How to Get Consistent Answers from AI: System Prompts

Chapter 4

Building AI agents and automated workflows

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Lessons

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What Is an AI Agent, and How Is It Different from Regular AI?

The Difference Between Traditional Workflow Automation and AI Agents

The Core Components of AI Agent Architecture

APIs: The Channel Through Which Agents Connect to External Systems

Zapier and Make: Bringing Workflow Automation to the Masses

Introduction to n8n and a Practical Usage Guide

n8n Practice: Building Your First Automation Workflow

OpenClaw: An Open-Source AI Agent Framework

Closing Thoughts