AI Foundations

Most people using AI at work right now are guessing. They paste something into ChatGPT, get a mediocre answer, and conclude the tool is overhyped. The tool is fine. What is missing is a mental model of what is actually happening when they hit enter.

That is what this course builds, in plain English, starting from zero. No math, no code, no assumed background. Just the working knowledge that separates people who use AI well from people who poke at it.

What you actually learn

The first section sorts out the vocabulary everyone uses loosely: what AI is, how it differs from automation and ordinary scripts, and what the main types (LLMs, computer vision) are good at. There is a lesson specifically on separating what AI can do today from the hype, because knowing the limits is half of being credible. It closes with why companies are hiring AI-skilled workers in the first place.

Then it goes under the hood, gently. What a model is. Training versus inference. Tokens, prompts, and outputs, and why the token part matters more than people expect: it is how everything gets priced. There is a full lesson on the cost of AI, so when someone asks "what would this cost us at scale," you have a real answer instead of a shrug. APIs get their own lesson too, because that is how AI actually plugs into products and workflows.

The tools, matched to the job

The tools section is organized by category, which is the honest way to teach it since individual products change monthly. Writing generation, image generation, and video generation each get covered on their own terms. Then the two categories with the most career weight: automation platforms (Zapier, Make, N8N) and AI coding tools (Cursor, Copilot). You leave knowing which shelf to reach for, whatever the tool of the month happens to be.

Prompting is a learnable skill

The prompting section is the most immediately useful part. You see bad prompts next to good ones, and the difference is rarely subtle: the bad ones are vague requests, the good ones are briefs. Then you learn a simple framework: give the model a role, a task, and a format. Chain of thought gets explained without the academic wrapping, because the underlying idea (asking the model to reason before answering) is simple once someone strips the jargon off it.

The section ends with reusable prompts, which is where this stops being a party trick and becomes a work asset. A prompt you refine once and use fifty times is a small piece of infrastructure, and building a personal library of them is one of the cheapest productivity upgrades available right now.

One honest note: this is a foundations course, and I mean that word. If you already build automations for clients, you want the next level up. This is for the person who needs to go from vague awareness to real literacy before that step.

Where it leads

The final section is about momentum: how to start applying AI skills immediately and how to transition into AI automations, which is where the career money is. If that path interests you, read what AI automation actually is, look at the AI Automations career path, and when you are ready to build, the AI Automations course picks up exactly where this one ends.