Understanding AI properly — from prediction to prompting to safe, practical use.
Who are you, and what brought you here today?
The philosophy behind AI Fundamentals
Total course length across 7 modules — we'll cover the highlights today
Building accurate mental models and separating capability from hype.
A practical definition that actually makes sense.
When is something AI, and when is it just good programming?
For each scenario below, decide: is it AI, automation, analytics, or regular software? 1. Spam filter that learns from your behaviour 2. Out-of-office reply 3. Netflix recommending shows 4. A pivot table in Excel 5. Siri answering a question 6. Scheduled social media posts
Data in → patterns learned → predictions out.
Marketing says one thing. Reality often says another.
Find an AI product or service online (or think of one you've seen marketed). 1. What does the marketing claim? 2. What is it probably actually doing? 3. How would you verify the claim? Discuss with your neighbour.
Prediction, tokens, context windows, and why outputs can be wrong.
The single most important thing to know about LLMs.
The key mental model for LLM use
Complete these sentences — predict the next word: "To be or not to ___" "The quick brown fox jumps over the ___" "In a galaxy far, far ___" You just did what an LLM does — used patterns to predict. Now imagine doing that with billions of sentences of training data.
How prompts are processed and why length matters.
Why the same prompt gives different answers.
Open ChatGPT (or any model) and ask the exact same question three times: "Give me three team-building ideas for a team of 10 people" Are the results the same each time? What varies? What stays consistent?
The architecture that made modern AI possible.
Choosing the right model and tool for the job.
Choosing by task, not brand loyalty.
Choose by what you need done, not by brand.
Based on what you've learned, create a personal "AI Starter Stack": 1. Primary tool: (for everyday tasks) 2. Secondary tool: (for specific needs) 3. Rules: (what you WILL and WON'T use AI for) Write it on paper or in a note — this is your v1 policy.
Writing clear prompts that get useful results.
The single most useful skill in AI.
BAD: "Write me a marketing plan" GOOD: "Act as a digital marketing strategist with 10 years experience in hospitality. I run a 40-seat cafe in Newtown, Sydney. We're strong on weekdays but quiet on weekends. Our target audience is 25-40 year olds who work from home. Please create a 4-week social media plan focused on increasing weekend foot traffic. Format as a weekly calendar with specific post ideas, captions, and best posting times for Instagram and Facebook."
Rewrite each of these vague prompts into effective ones using RCTF: 1. "Help me with my presentation" 2. "Write an email" 3. "Give me ideas" 4. "Summarise this document" Share your best rewrite with the group.
Show the AI what you want — don't just tell it.
Classify these customer enquiries as Sales, Support, or Billing: Examples: "How much does the Pro plan cost?" → Sales "My login isn't working" → Support "I was charged twice this month" → Billing Now classify: "Can I upgrade from the Basic plan?" "The export button gives an error" "Do you offer team discounts?"
Templates you can use immediately.
Think about your actual job. Create 3 reusable prompt templates for tasks you do regularly: 1. A task you do daily 2. A task you do weekly 3. A task that takes too long manually Use RCTF structure. Keep them — these are your AI shortcuts.
Breaking complex tasks into steps.
Step 1: "Give me 5 blog post angles about [topic] for [audience]" Step 2: "Expand angle #3 into a detailed outline with H2 headings" Step 3: "Write the introduction paragraph — hook the reader in 3 sentences" Step 4: "Now write section 2, keeping the tone conversational and under 200 words" Each step is small, focused, and easy to redirect.
Take one complex task from your work and break it into a 3-4 step prompt chain: 1. What info do you need first? 2. What decision comes next? 3. What's the final output? Test it in your preferred AI tool.
Checking outputs, reducing errors, and spotting failure modes.
Confident-sounding inaccuracies — not bugs, not lies.
No model is immune — verification is always your responsibility
Ask your AI tool a specific factual question about something you know well: - A specific statistic from your industry - A date/fact about your organisation - Details of a real event you attended Did it get it right? If wrong, how confident did it sound?
Practical habits for checking AI outputs.
Take an AI-generated response you've received today and run it through the verification checklist: 1. Can you verify the key claims? 2. Are any numbers/dates/names accurate? 3. Do the cited sources (if any) actually exist? Score: how many checks passed?
Simple rubrics — not complex engineering.
Pick one recurring task you plan to use AI for. Define: 1. Three things that must be accurate 2. The required format 3. One thing that would make the output unacceptable This is your evaluation rubric for that task.
Using AI responsibly, safely, and ethically.
AI can help or harm — the choice is in how we use it.
What should and should not go into AI systems.
Think about the last 5 things you put into an AI tool: 1. Did any of them contain personal data? 2. Did any contain confidential business info? 3. Is model training turned off in your settings? Check your settings now. Turn off training if needed.
What you can and can't do with AI-generated content.
Lightweight rules that keep you safe.
Create a one-page AI usage policy for your team or yourself: 1. List 3 GREEN tasks (free to use AI) 2. List 3 AMBER tasks (AI + human review) 3. List 3 RED tasks (no AI or extreme caution) 4. One rule about data handling 5. One rule about disclosure Keep it simple and practical.
Turning understanding into daily practice.
Two or three high-value use cases that save real time.
Map your actual work week. For each day: 1. What task takes the most time? 2. Could AI help with any part of it? 3. What would verification look like? Pick your top 2-3 use cases. These become your personal AI operating system.
Helping others adopt AI without forcing hype-led rollouts.
Draft a team AI pilot brief: 1. Choose one task your team does repeatedly 2. Choose a tool 3. Define success (how will you know it worked?) 4. Set guardrails (what's off-limits?) 5. Set a timeline (2-4 weeks) Present your pilot to the group.
Solve a real task using everything you've learned.
Choose a real task and complete it end-to-end with AI: Requirements: ✓ Use RCTF prompting ✓ Chain at least 2 prompts ✓ Verify the output ✓ Note what you'd improve Ideas: write a proposal, analyse a document, create a plan, draft communications, solve a problem.
Present your capstone to the group (3-5 min): 1. What task did you solve? 2. What tool(s) did you use? 3. How did you prompt it? 4. How did you verify the output? 5. What would you do differently?
Your pathway from here.
Complete all modules + capstone + quiz = AI Fundamentals Certificate
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