From Words to Numbers: How Embeddings, Probability, and Prompts Create Intelligence
Free Book: December 23–27, 2025
Artificial Intelligence is everywhere.
It writes emails, summarizes documents, answers questions, generates images, recommends content, and increasingly feels like something you can talk to. For some people, that feels exciting. For others, confusing, unsettling, or even intimidating.
The real problem isn’t AI itself.
The problem is how AI is usually explained.
AI is often presented in extremes:
- As a magical thinking machine
- Or as a dangerous force replacing human intelligence
Both views are wrong.
That’s why I wrote How AI Really Works — and to celebrate the release, the book is free from December 23 to December 27, 2025.
📘 Amazon US:
https://www.amazon.com/dp/B0G8Y9K2P5
📘 Amazon Canada:
https://www.amazon.ca/dp/B0G8Y9K2P5
Why Most AI Explanations Fail
Most AI books and articles fall into one of three traps:
1) Too Technical
Heavy math, algorithms, and jargon that exclude beginners.
2) Tool-Focused
Teaching how to use today’s AI tools without explaining what’s actually happening underneath.
3) Sensational
Fear, hype, promises, and speculation about the future—without grounding in reality.
How AI Really Works takes a different approach.
It explains AI using:
- Plain English
- Clear mental models
- Everyday analogies
- Practical, real-world examples
No programming.
No math background.
No hype.
The goal isn’t to turn you into an AI engineer.
The goal is to help you think clearly about AI.
That skill lasts longer than any tool.
The Core Idea: AI Is Not Intelligent (In the Human Sense)
Modern AI does not:
- Think
- Understand
- Know things
- Have intentions
- Possess awareness
Instead, it is extraordinarily good at:
- Recognizing patterns
- Representing meaning as numbers
- Navigating similarity
- Predicting what comes next
Once you understand this, AI stops feeling mysterious.
And once the mystery is gone, fear and hype lose their power.
A Simple Mental Model: AI as a Probability Engine
Here is the most important idea in the book:
AI is a probability engine, not a knowledge engine.
When you type a prompt, the AI is not:
- Looking up answers
- Checking facts
- Reasoning like a human
It is doing this instead:
“Based on everything I’ve seen before, what is the most likely next word… then the next… then the next?”
That’s it.
This explains:
- Why AI sounds confident
- Why it can be wrong
- Why it can invent sources
- Why prompts matter—but aren’t commands
BONUS CONTENT (Website Exclusive)
Below is additional bonus learning content you won’t find directly in the chapter—designed to deepen understanding and help you use AI better immediately.
🔍 Bonus Mental Model: The Map vs. the Territory
AI does not understand the world.
It understands a map of patterns about the world.
Think of it like this:
- The territory is reality (facts, truth, meaning)
- The map is statistical relationships in language
AI only has the map.
Humans must validate the territory.
This is why:
- AI can sound right and be wrong
- AI can explain concepts it doesn’t understand
- AI must always be reviewed by a human
🧠 Bonus Exercise: Rewrite the Illusion (Expanded)
Rewrite these common phrases into accurate statements:
❌ “The AI understands my question.”
✅ “The AI is predicting a response based on language patterns similar to my question.”
❌ “The AI knows a lot about history.”
✅ “The AI has learned patterns from historical text but does not know what is true.”
❌ “The AI decided to answer this way.”
✅ “The AI generated the most statistically likely continuation.”
If you can rewrite statements like this naturally, your AI intuition is improving.
⚠️ Bonus Reality Check: Why AI Sounds Confident
AI does not:
- Know when it is wrong
- Feel uncertain
- Signal doubt reliably
Confident language appears frequently in training data.
Uncertainty appears less often.
So AI often produces:
- Fluent explanations
- Assertive tone
- Polished structure
Even when the content is incorrect.
Confidence is not accuracy.
That responsibility always remains human.
✅ Bonus Best Practices (Start Using These Today)
When working with AI:
- Never assume correctness
- Ask why, not just what
- Request sources—and verify them
- Break complex tasks into steps
- Treat AI like a junior assistant, not an expert
AI amplifies:
- Clear thinking
- Good judgment
- Curiosity
But it also amplifies:
- Confusion
- Bias
- Poor assumptions
Understanding how AI works lets you stay in control.
Who This Book Is For
This book is written for:
- Curious beginners
- Educators and students
- Professionals using AI at work
- Writers, creators, and knowledge workers
- Anyone who uses AI tools and wants to understand what’s happening under the hood
You do not need:
- Programming experience
- Math or machine learning knowledge
- A technical background
If you can ask questions and think critically, you’re ready.
Free for a Limited Time
📘 How AI Really Works
🗓 Free: December 23–27, 2025
- Amazon US: https://www.amazon.com/dp/B0G8Y9K2P5
- Amazon Canada: https://www.amazon.ca/dp/B0G8Y9K2P5
AI is not replacing intelligence.
It’s revealing how important human intelligence still is.
Once you understand AI,
you stop fearing it,
stop worshipping it,
and start using it wisely.
Introduction — Understanding AI Without the Myth
Artificial Intelligence is everywhere.
It writes emails, summarizes documents, answers questions, generates images, recommends content, and increasingly feels like something you can talk to. For many people, this experience is exciting. For others, it is confusing, unsettling, or even intimidating.
The problem is not AI itself.
The problem is how AI is usually explained.
AI is often presented in extremes:
- As a magical, thinking machine
- Or as a dangerous force replacing human intelligence
Both views are wrong.
This book exists to replace myth and hype with understanding.
Why This Book Is Different
Most AI books fall into one of three categories:
- Highly technical
Full of math, algorithms, and jargon that exclude beginners. - Tool-focused
Teaching how to use today’s AI tools without explaining how they actually work. - Speculative or sensational
Focused on fear, promises, or the future rather than reality.
This book takes a different approach.
It explains how AI really works, using:
- Plain English
- Clear mental models
- Everyday analogies
- Practical examples
- No required technical background
You will not learn how to “build an AI model.”
You will learn how to think clearly about AI.
That skill lasts longer than any tool.
The Core Idea of This Book
At its core, modern AI is not intelligent in the human sense.
It does not:
- Think
- Understand
- Know things
- Have intentions
- Possess awareness
Instead, modern AI is extraordinarily good at:
- Recognizing patterns
- Representing meaning as numbers
- Navigating similarity
- Predicting what comes next
Once you understand this, AI stops feeling mysterious.
And once the mystery is gone, fear and hype lose their power.
What You Will Learn
This book will help you understand:
- What AI is and is not
- How AI learns from data without understanding it
- Why words must be converted into numbers
- What embeddings are and why they matter
- How vector space acts as a “map of meaning”
- What really happens when you press Enter
- Why prompts matter—and why they are not commands
- Why AI sometimes makes things up
- How bias enters AI systems
- How to use AI safely, responsibly, and effectively
- How to think with AI instead of relying on it
You will also learn how to:
- Write clearer prompts
- Spot hallucinations
- Recover from bad outputs
- Use AI as a learning and thinking partner
- Stay relevant as AI tools evolve
What This Book Will Not Do
This book will not:
- Teach you to code AI models
- Turn you into an AI engineer
- Promise perfect answers
- Claim AI understands you
- Encourage blind trust in technology
Instead, it will give you something more valuable:
Confidence rooted in understanding.
Who This Book Is For
This book is for:
- Curious beginners
- Educators and students
- Professionals using AI at work
- Creators and writers
- Anyone who uses AI tools and wants to understand what’s happening under the hood
You do not need:
- A technical background
- Programming experience
- Knowledge of mathematics or machine learning
If you can ask questions and think critically, you are ready.
How to Read This Book
You can read this book:
- From start to finish
- One chapter at a time
- As a reference when something confuses you
Each chapter:
- Builds on the previous one
- Introduces one major idea
- Uses repetition intentionally to reinforce understanding
- Ends with exercises and a short quiz
You are encouraged to:
- Pause and reflect
- Try the exercises
- Test ideas with real AI tools
- Question what you read (including this book)
That is how real learning happens.
A Note on Language and Simplicity
This book intentionally avoids:
- Heavy jargon
- Mathematical notation
- Overly technical explanations
Not because those things are unimportant,
but because they are not required to understand AI behavior.
You will learn how AI behaves, not how to implement it.
That distinction matters.
The Responsibility of the Reader
AI is a powerful amplifier.
It amplifies:
- Good thinking
- Clear intent
- Curiosity
But it also amplifies:
- Confusion
- Bias
- Poor judgment
This book will show you how AI works.
What you do with that knowledge is up to you.
The responsibility for:
- Truth
- Ethics
- Decisions
- Impact
Always remains human.
Before We Begin
One final thought before Chapter 1:
AI is not replacing intelligence.
It is revealing how important human intelligence still is.
Once you understand AI,
you stop fearing it,
stop worshipping it,
and start using it wisely.
Let’s begin.
Chapter 1 — What AI Is (And What It Is Not)
Removing the Mystery Before Learning the Mechanics
Chapter Goal
By the end of this chapter, you will:
- Understand what AI actually is
- Stop thinking of AI as a “thinking machine”
- Learn why AI feels intelligent even though it isn’t
- Build the correct mental model for everything that follows in this book
This chapter is intentionally non-technical. We’re building intuition first.
1.1 The Biggest Misunderstanding About AI
Most people think AI is:
- A digital brain
- A thinking entity
- Something that understands language
- A system with opinions or intentions
None of those are true.
AI does not:
- Think
- Understand
- Know things
- Feel confident or uncertain
- Have awareness or intent
Yet it often sounds like it does.
Why?
Because modern AI is extremely good at predicting patterns in language.
1.2 What AI Really Is (In Plain English)
At its core, modern AI is:
A system that predicts what comes next based on patterns it learned from massive amounts of data.
That’s it.
When you type a prompt into an AI system, it is not:
- Looking up answers
- Searching the internet (unless explicitly designed to)
- Checking facts
- Reasoning like a human
Instead, it is doing this:
“Based on everything I’ve seen before, what is the most likely next word, then the next, then the next?”
It builds responses one piece at a time, based on probability.
1.3 Why AI Feels So Intelligent
AI feels intelligent because:
- Human language has patterns
- Meaning can be statistically modeled
- Large datasets capture human reasoning styles
- Responses are fluent and confident
Think of AI like this:
Someone who has read billions of sentences and learned how humans usually respond — but has no idea why they respond that way.
This creates the illusion of understanding.
1.4 A Simple Analogy: The Super Autocomplete
You already use AI every day.
Examples:
- Your phone suggesting the next word
- Gmail finishing your sentence
- Netflix recommending a show
- Spotify suggesting music
Generative AI is just:
Autocomplete on steroids
Instead of predicting:
- One word
It predicts: - Entire paragraphs
- Explanations
- Conversations
But the core mechanism is the same.
1.5 AI Does Not Understand Language
This is one of the most important ideas in the entire book.
AI does not understand:
- Words
- Concepts
- Meaning
- Truth
Instead, it understands relationships between symbols.
For example:
- It knows “fire” often appears near “hot”
- It knows “Paris” often appears near “France”
- It knows “if… then…” patterns
But it does not know what fire is
Or where Paris is
Or why logic works
1.6 Why AI Can Sound Confident — And Be Wrong
AI does not know when it is wrong.
It cannot:
- Doubt itself
- Check its memory
- Verify accuracy
- Signal uncertainty reliably
It produces responses that sound right because:
- Confident language appears frequently in training data
- Humans often explain things assertively
- Uncertainty is less common in written examples
This is why AI can:
- Invent sources
- Fabricate explanations
- Confidently explain things that do not exist
This behavior is often called hallucination — but it’s really just pattern completion without grounding.
1.7 Important Mental Model: AI as a Probability Engine
Here is the mental model you should keep for the rest of this book:
AI is a probability engine, not a knowledge engine.
It answers:
- What usually comes next?
- What sounds most plausible?
- What fits the pattern?
Not:
- What is true?
- What is correct?
- What is ethical?
That responsibility stays with you.
1.8 Why This Understanding Matters
If you believe AI:
- Thinks like you
You will trust it too much.
If you believe AI:
- Is “just autocomplete”
You will use it poorly.
The correct view is in the middle:
- AI is powerful
- AI is useful
- AI is limited
- AI needs guidance
Understanding this makes you:
- A better prompter
- A safer user
- A smarter collaborator
1.9 Exercises
Exercise 1 — Rewrite the Myth
Rewrite each statement to be accurate:
- “AI understands my question.”
- “The AI knows a lot about history.”
- “The AI decided to answer this way.”
Hint: Replace “understands,” “knows,” and “decided.”
Exercise 2 — Spot the Illusion
Think of a time AI:
- Sounded confident
- Was later shown to be wrong
Write:
- What it said
- Why it sounded believable
- What assumption you made
Exercise 3 — Daily AI Awareness
For one day, notice:
- Every AI-assisted suggestion you encounter
- Autocomplete
- Recommendations
- Smart replies
Ask yourself:
“What pattern is this system predicting?”
1.10 Tips for Working With AI (Starting Now)
- Never assume correctness
- Ask for explanations
- Request sources (and verify them)
- Break complex tasks into steps
- Treat AI like a junior assistant, not an expert
Chapter 1 Quiz — Check Your Understanding
Question 1
What best describes modern generative AI?
A) A thinking machine
B) A digital brain
C) A probability-based pattern predictor
D) A self-aware system
Correct Answer: C
Explanation: AI predicts likely outputs based on patterns, not understanding.
Question 2
Why does AI feel intelligent?
A) It understands language
B) It has consciousness
C) It recognizes and reproduces patterns well
D) It reasons like humans
Correct Answer: C
Explanation: Pattern recognition creates the illusion of intelligence.
Question 3
What does AI not do?
A) Generate text
B) Predict word sequences
C) Understand meaning
D) Mimic human language
Correct Answer: C
Explanation: AI does not understand meaning.
Question 4
Why can AI confidently give incorrect answers?
A) It is lying
B) It lacks awareness of correctness
C) It wants to persuade you
D) It stores false data
Correct Answer: B
Explanation: AI does not know when it is wrong.
Question 5
Which is the best analogy for generative AI?
A) A digital encyclopedia
B) A thinking brain
C) A super-powered autocomplete
D) A search engine
Correct Answer: C
Question 6
What does AI rely on to generate responses?
A) Logic rules
B) Stored facts
C) Probability patterns
D) Human judgment
Correct Answer: C
Question 7
Why is “AI understands you” an illusion?
A) AI hides its reasoning
B) AI predicts language without comprehension
C) AI pretends to understand
D) AI checks emotions
Correct Answer: B
Question 8
Which responsibility remains with the human user?
A) Generating responses
B) Formatting text
C) Verifying accuracy
D) Tokenizing input
Correct Answer: C
Question 9
What is the most accurate way to think about AI?
A) As a digital person
B) As a thinking assistant
C) As a probability engine
D) As a knowledge base
Correct Answer: C
Question 10
Why is this understanding critical before learning prompts?
A) Prompts require coding
B) Prompts only work with facts
C) Prompts guide probability paths
D) Prompts increase AI intelligence
Correct Answer: C
Chapter 1 Summary
AI is not magic.
AI is not a mind.
AI is not conscious.
AI is:
- Pattern-based
- Probability-driven
- Incredibly useful
- Deeply limited
Once you understand this, everything else — embeddings, vector space, prompts, and applications — becomes much easier to grasp.