Start here
The foundations that make every later guide easier. Read this pillar first if you are new to AI.
If you are new to AI, read these in order. The first guide is the 30-minute starter path that links to every other piece. The rest go deeper on the questions everyone hits early: which model to use, what AI gets wrong, what is safe to share at work, and how to prompt.
Getting started with AI: a 30-minute starter path
A guided reading order if you are new to AI. What to learn first, what to skip, and the three habits that separate people who get value from AI from people who give up after a week.
What is an LLM, really?
The mental model that fixes most prompting confusion. Why models hallucinate, what context windows are, and how tokens map to cost.
What AI is good at, and what it still gets wrong
A blunt capability map. The categories of work where AI is reliable, the categories where it bluffs, and the in-between where it works if you verify.
Which AI should I use? Claude vs ChatGPT vs Gemini
A working decision matrix, not a feature list. Strengths, weaknesses, pricing, and the kind of work each model quietly handles best. Plus a quick "if your goal is X, start with Y" guide.
AI at work: what is safe to share, what is not
Customer data, source code, contracts, secrets. The practical rules for using AI without leaking what your employer cares about.
Free AI courses worth your time (from the people who built it)
A short, opinionated list of the free training that's actually worth the hours. From Anthropic, OpenAI, Google, Hugging Face, and the University of Helsinki. With what each is good for and what to skip.
How to prompt: 5 patterns that work in any model
Give context, show examples, demand structure, iterate, assign a role. Before/after examples for each.
Verify and trust
Now that you understand the basics, learn how to check AI output before you act on it.