AI Fundamentals

The opening module sets the distinctions that actually matter, walks through just enough history for the present moment to make sense, and introduces the mental model you will carry through the rest of the course, along with an honest answer to why all of this matters right now.

• 01

  What is AI, really?

  You get a plain English definition of artificial intelligence, a clear line between it and ordinary automation, and an honest explanation of why the word gets stretched to cover almost anything.

• 02

  A short history (so today makes sense)

  You travel from the 1956 Dartmouth gathering through to the era of AI agents, with just enough history to understand how we arrived at the present moment and why it feels so different from what came before.

• 03

  What modern AI isn't

  You meet the five misconceptions that most often trip up new users, so that you have your boundaries clear in your mind before you start exploring what these tools can genuinely do.

• 04

  The mental model

  You are given a working metaphor for how a language model actually produces its output, and this becomes the lens through which you will read everything that follows in the course.

• 05

  Why this matters now

  You get an honest account of what genuinely shifted over the last four years, what has stayed exactly the same, and why even thoughtful and cautious people can no longer afford to look away.

This module opens up the parts most explanations skip, covering tokens, the difference between training and using a model, context windows, and a dedicated lesson on why models make things up, all in plain words with no maths and no jargon, and a fair bit deeper than the usual magic black box treatment.

• 01

  Tokens, the building blocks

  You learn how an AI reads text quite differently from the way you and I read it, and what that quiet difference means in practice for the prompts you write.

• 02

  Training versus using

  You learn the difference between the moment a model is built and the moment you prompt it, and you get a straight answer about what does and does not travel back to the company behind it.

• 03

  Context windows and memory

  You learn what an AI can hold in mind within a single conversation and what it carries across separate ones, so you stop expecting a memory it simply does not have.

• 04

  Why AI hallucinates

  This dedicated lesson takes on the most important failure mode of all, walking you through what hallucination looks like, why it happens, and the practical habits that keep it from catching you out.

• 05

  The mental model, refined

  You return to the metaphor from the opening module and fill it out with everything you have since learned about tokens, training, context and hallucination, so the picture finally feels complete.

Here you get an even handed look at Claude, ChatGPT, Gemini, Microsoft Copilot and Perplexity, with no allegiances and no hype, so that you can choose the right tool for the task in front of you rather than the one with the loudest marketing.

• 01

  ChatGPT (OpenAI)

  You learn what ChatGPT does well and where it falls down, what each paid tier actually gives you, and the kinds of tasks where it is the natural tool to reach for.

• 02

  Claude (Anthropic)

  You learn what Claude does well and where it struggles, when it is the better choice over ChatGPT, and how it tends to handle an Australian context.

• 03

  Gemini and Microsoft Copilot

  You learn where Gemini and Copilot genuinely earn their place, especially if your working day already lives inside Google Workspace or Microsoft 365.

• 04

  Perplexity and the search-AIs

  You learn when an AI grounded in live search will serve you better than a general chatbot, and how to check the citations it gives you rather than taking them on faith.

• 05

  When to use which

  You get a simple decision guide for matching the right tool to the task at hand, along with the handy trick of running two AIs side by side on the same question.

This module treats prompting as a genuine craft, covering the four ingredients of a good prompt, learning from examples, thinking that works through a problem one step at a time, iterating the way a maker would, and learning to spot weak output before you put any trust in it.

• 01

  The four ingredients of a good prompt

  You learn the four ingredients that make a prompt work, namely role, task, context and format, and why that structure consistently beats a hurried one line question.

• 02

  Examples beat instructions

  You learn how showing the model a worked example often guides it far better than describing what you want, and when a single good example will do more than a whole paragraph of instruction.

• 03

  Thinking step by step

  You learn how to ask a model to work through a problem one step at a time, along with its extended thinking modes, and you get an honest sense of when this helps and when it simply does not.

• 04

  Iterating like a craftsperson

  You learn to treat prompting as a loop rather than a single attempt, generating a few options, comparing them, and refining your way towards something genuinely good.

• 05

  Spotting bad outputs

  You build the verification habits that save you from embarrassment, which is also the quiet beginning of learning to evaluate AI output properly.

The final module is about living alongside AI day to day, covering practical workflows, the privacy basics with a primer on the OAIC guidance, the eight Australian AI Ethics Principles explained plainly, the real risks around defamation and intellectual property, and an honest look at jobs and what is coming, without the doom and without the hype.

• 01

  Building a daily AI workflow

  You get a concrete weekly routine that puts AI to work three or four times a day on real tasks, so the tools become a natural part of how you get things done.

• 02

  Privacy basics and the OAIC primer

  You learn what should never be pasted into a chat in the first place, and what the OAIC guidance actually means for you as an individual rather than a large organisation.

• 03

  The Australian AI Ethics Principles in plain English

  You walk through the eight voluntary principles one by one, what each one means for the way you use AI, and why they still matter to you even though no law forces them on anyone.

• 04

  Bias, fairness, and your responsibility

  You learn why AI output can carry bias, what you can practically do about it, and you get an introduction to the risks around defamation and intellectual property under Australian law.

• 05

  Jobs, skills, and what is coming

  You get an honest reading of the next two or three years for your work, without the doom and without the hype, so you can plan with a clear head rather than a racing one.