Why I'm Teaching This
Before we get into the material
I've been the person learning the new thing more times than I can count — PCs and C++ in a plumbing store back office in the 1990s, Scrum when almost nobody in Seattle had heard of it, Test-Driven Development when most teams thought it was optional. Artificial Intelligence is the biggest shift I've seen yet, and I've spent the last several years learning it the same way I've learned everything else: by using it daily, breaking things, and figuring out what actually works.
This lesson is the starting point I wish someone had handed me. It won't turn you into a machine learning engineer, and it doesn't need to. It will get you using AI competently, confidently, and without the anxiety that so much of the hype seems designed to create.
The Fundamentals
What "AI" actually means, in plain language
Most of the confusion about AI starts because the word gets used for several different things at once. Here are the three terms you'll run into constantly — no jargon, no equations.
| Term |
What It Actually Means |
| Machine Learning |
The broad idea of software that improves at a task by learning patterns from examples, rather than being explicitly programmed step by step. It's the umbrella everything else sits under. |
| Large Language Models |
A type of machine learning model trained on enormous amounts of text so it can predict and generate language. ChatGPT, Claude, and Gemini are all built on large language models. They predict what comes next — they don't "know" facts the way a database does. |
| Generative AI |
Any AI that creates new content — text, images, code, audio — instead of just classifying or predicting a number. This is the category that's changed daily work the fastest, and it's what the rest of this course focuses on. |
Outcomes
By the end of this lesson, you'll be able to
- ✓Tell the difference between a chatbot, an AI assistant, and an "agent" — and know which one you're actually using.
- ✓Write a prompt that gets a useful answer on the first try, instead of three vague attempts.
- ✓Recognize when an AI is likely to be wrong, and verify its output before you trust it.
- ✓Pick one real task from your own work or life and get AI to help with it today.
The Core Lesson
Five things to understand before you touch a tool
Just like I never let a Scrum team run a sprint without understanding why the ceremonies exist, I don't want you opening an AI tool without understanding these five ideas first. They'll save you more frustration than any feature list.
| 1. AI predicts, it doesn't know. |
A language model generates the statistically likely next words based on its training. That's why it can sound completely confident while being completely wrong. Treat every factual claim as something to verify. |
| 2. Vague input gives vague output. |
"Write me a marketing plan" gets a generic answer. A one-page plan for a specific business, audience, and region gets something you can actually use. Specificity is the whole skill. |
| 3. You are still the expert. |
AI is a very capable junior collaborator with no judgment about your context. It doesn't replace your years of experience — it replaces the blank page. Keep your hands on the decisions that matter. |
| 4. Iteration beats the perfect prompt. |
Treat every AI conversation like a retrospective: ask, look at what you got, adjust, ask again. Two or three rounds almost always beats one "perfect" attempt. |
| 5. Small, real tasks beat big, abstract ones. |
Don't start by asking AI to "transform your business." Start by asking it to draft one email or explain one concept. Confidence is built from small wins, same as with any new skill. |
Hands-On Exercise
10 minutes, right now
Open whichever AI tool you have — ChatGPT, Claude, Gemini, or Copilot all work fine. Pick one small, real task you actually need done this week. Give it real context: who it's for, what outcome you want, any constraints. Read what comes back critically, then ask for one specific change. That loop — context, draft, critique, refine — is 90% of working well with AI, and everything else in this course builds on it.
Getting Started: Do's and Don'ts
The habits that separate confident beginners from frustrated ones
DO Give the AI real context — audience, goal, constraints.
DO Verify anything factual before you rely on it.
DO Start with one small, real task this week.
DO Ask for a second draft instead of accepting the first.
DO Stick with one tool for a week before comparing others.
DON'T Treat the first answer as automatically correct.
DON'T Ask vague, one-line questions and expect a great result.
DON'T Hand over decisions that require your judgment.
DON'T Start with a huge, abstract goal like "transform my business."
DON'T Tool-hop before you've given one tool a real chance.
Reminder: AI is a capable collaborator, not a replacement for your judgment. The habits above matter more than which specific tool you choose.
Coming Up Next
Lesson 2: Prompting Like You Mean It
Next time, we'll go deeper on the single highest-leverage skill in this whole course: writing prompts that consistently get you what you need — whether you're drafting code, writing a test plan, or coaching a team through a hard conversation.