Three stages, from zero to using AI in daily work
You don't have to learn it all at once. Follow the order — every stage says exactly what you'll be able to do when you finish
Understand it, dare to ask
Build an accurate mental model first: know what AI can and cannot do.
- Sign up for one main AI assistant (ChatGPT, Claude or Gemini)
- Understand what a large language model does and why it gets things wrong
- Learn to turn a one-line request into a full task description
- Set your own data safety line: what you will never paste
Use it fluently, save real time
Put AI into the work you actually do every day, so the time you save becomes visible.
- Build 5–10 prompt templates for your own job
- Learn to upload files, read long documents and organise data
- Tell which tasks suit AI and which you should do yourself
- Build a checking habit: how to verify what AI tells you
Build workflows, scale your output
Go from asking one question at a time to a workflow that runs itself.
- Build a personal knowledge base so AI answers from your own material
- Get to know agents and automation tools, and hand off repetitive work
- Build small tools without coding (intro to vibe coding)
- Roll it out to your team: process, guidelines and training
Six topics that take AI from "heard of it" to "use it daily"
All free, and all rewritten from scratch for people without a technical background
AI tool guide
Chat, writing, slides, images, video, research, translation — for every category we've picked 2–3 tools that are good enough, and we tell you straight who each one suits and how far the free tier goes. No more endless comparison.
Pick a tool to startA prompt library you can copy straight away
Prompt templates organised by job and situation: emails, slides, meeting notes, reports, lesson prep, job hunting. Each one comes with "how to make it yours" — copy, paste, done.
Find your promptFrom one-off questions to workflows
Learn to hand repetitive work to AI: file organisation, data consolidation, weekly report automation. Get to know agents, knowledge bases (RAG) and automation tools, and build your own flow without writing code.
Make AI a coworkerAI basics that actually make sense
What is a large language model? Why does AI state wrong things so confidently? What do context, tokens and hallucination really mean? Explained with everyday analogies, no math.
Get the idea in 3 minutesReal use cases at work and at home
Marketing, HR, admin, sales, teachers, students, parents, freelancers — every role gets a list of AI uses you can apply today, with real examples.
Find your jobSafety, privacy and judgment
What should never be pasted into AI? How do you verify its answers? What to watch for when using it inside a company? Know the risks and you can rely on it for the long run.
Protect yourself firstFeatured reading
Every post starts from a problem you'll actually run into, and ends with something you can try
No math and no code — just analogies you'll understand for how large language models work, what they're good at, and why they get things wrong. After this you'll know how to talk to AI.
Same question, so why do other people get great answers while you get waffle? Six principles covering role, context, format and examples, so AI gives you what you want the first time.
The three big assistants each have their own temperament. This post sorts them by what you actually do: writing, long documents, research, slides, coding — who to use for what, and whether the free tier is enough.
The legal clauses, figures and citations AI gives you can be entirely made up. Understand where hallucinations come from and build a 30-second checking routine, so you can move fast and still trust the result.