How ChatGPT Actually Works LLM Principles Without Code

Tokens, embeddings, temperature, hallucination, context. The 5 principles behind every AI tool. No code.
What you'll learn

---

Explain exactly what happens inside ChatGPT in the half-second between pressing Enter and seeing the first word appear.
Predict when AI will fail, and name the cause: tokenization, probability, embeddings, or the context window.
Spot hallucination before it reaches your client or your boss, using a repeatable verification workflow.
Manage long conversations and long documents so the model stops losing the information that matters most.
Use temperature to switch between consistent factual output and creative output, even in web apps with no settings.
Understand why AI cannot count the letters in "strawberry", and what that one failure reveals about every other one.
Requirements

---

No programming experience required. This course contains no code of any kind.
A free ChatGPT, Claude, or Gemini account for the hands-on labs. No paid plan needed.
Basic comfort using a web browser. That is the entire technical prerequisite.
Curiosity about why AI behaves the way it does, not just which prompt to copy.
Description

---

This course contains the use of artificial intelligence.
Specifically: the narration is an AI-generated voice.
The curriculum, the teaching material, the examples, and the hands-on labs are the instructor's own.
You already use ChatGPT every day. So why does it still surprise you?
It writes a brilliant summary, then invents a statistic that never existed..
You already use ChatGPT every day. So why does it still surprise you?
It writes a brilliant summary, then invents a statistic that never existed. It follows your instructions perfectly for twenty messages, then forgets your name. It insists there are two r's in "strawberry."
Most AI courses answer this with more prompt templates. This one answers it with the machine itself.
This is a principles course, not a prompt course.
In three and a half hours, you'll learn the five mechanisms that produce every ChatGPT behavior you've ever seen - good and bad
Tokenization- how the model chops your text into pieces before it reads a single word
Embeddings- how "meaning" becomes a list of numbers, and why king − man + woman = queen
Probability- why the model predicts the next token instead of looking up an answer
Temperature- the dial between "boringly consistent" and "creatively wrong"
The context window - why long conversations lose the thread, and what to do about it
Once you can name the mechanism, AI failures stop being mysterious. You stop guessing which prompt trick might work. You start predicting what the model will do before you press Enter.
No code. None.
There is no Python, no API, no installation, no terminal. Every hands-on exercise runs in the ChatGPT, Claude, or Gemini web app you already have open. If you can use a browser, you can finish this course.
What you'll actually do
Each section ends with a lab you run yourself. You'll count the tokens in your own prompts.
You'll build two custom GPTs - one "low temperature," one "high temperature" - and watch the same question produce two different personalities. You'll push a conversation until the model visibly forgets, and find the exact point where it happens. You'll trigger a hallucination on purpose, so you know what one looks like from the inside.
Who this is for
Marketers, product managers, planners, writers, analysts, consultants, founders, teachers - anyone whose job now includes AI but whose training didn't. If you've ever had to explain to a colleague *why* the AI got it wrong and had nothing better than "it's just like that sometimes," this course is the answer.
Who this is not for
Engineers who want to build, fine-tune, or deploy models. There is no code here by design.
The tools will change. The principles won't.
GPT-4 replaced GPT-3.5. Something will replace what you're using today. Tokenization, embeddings, probability, temperature, and context windows have survived every one of those transitions and will survive the next. That's what you're buying: the layer underneath the tool.
By the end you'll have written your own AI operating principles - a one-page checklist of when to trust AI, when to verify it, and when not to use it at all.
See you in the first lecture.
Who this course is for

---

Marketers, PMs, planners, writers, and analysts who use ChatGPT daily but cannot explain why it sometimes fails.
Non-technical professionals tired of copying prompt templates who want to understand the machine underneath them.
Team leads deciding where AI does and does not belong in their team's workflow.
Students and career changers who want a conceptual foundation before taking a technical AI course.
Anyone who has to explain AI behavior to colleagues and wants a better answer than "it's just like that sometimes".
Homepage

---