6 Free Lessons.
No Strings Attached.
Real AI lessons written for real people. No jargon overload, no PhD required. Read these six - if you like them, you'll love what's behind the door.
What people mean by AI when they talk about your job
A plain map of the tools already sitting in your inbox, docs, and browser — and why “AI” is not a department you wait for.
- Name the AI already in a normal desk job without a glossary fight
- Tell narrow task-AI from the movie version that does not exist yet
- Pick one real task this week and run it through a chatbot you already have
The AI Glossary: Speak Robot Without a Translator
LLM, token, RAG, fine-tuning, agent... AI people love acronyms. Here's the 15 terms that actually matter, in plain English, with zero shame attached.
- Understand the 15 most important AI terms in plain English
- Explain what an LLM and a token are
- Know what hallucination and context window mean and why they matter
Two tools for work. That is enough this month.
Pick a general chatbot and the AI already inside the software you use. Ignore the buffet until a real task blocks you.
- Name a default chatbot and one in-app assistant you will actually open
- Use the company-approved tool when the data is not yours to shop around
- Compare two models on the same work packet instead of collecting logos
How AI Actually Works: Autocomplete on Steroids
No jargon, no math, no PhD required. Learn what a large language model really does under the hood - and why it's both impressive and slightly dumb.
- Explain how large language models work using the autocomplete metaphor
- Understand what training means without any math
- Explain why hallucinations happen and how to guard against them
Why the person who uses AI gets the next assignment
A calm look at what changes when one person on the team drafts with AI and another person still starts from a blank page.
- Describe the real risk: not “robots,” but a colleague who ships the same work faster
- Refuse fake percentages and unnamed studies when someone argues about AI
- Map three tasks in your role to draft / verify / still-human
Write a work prompt the way you write a ticket
Role, facts, fences, and what “done” looks like. The same discipline as a ticket — because a vague prompt is how you get a vague draft with your name on it.
- Write a prompt with role, audience, facts, and fences
- Ask for a format you can paste into the real tool (email, bullets, table-as-list)
- Mark guesses instead of letting the model invent a customer
Liked those? There's much more inside.
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Agentic Workflows
Build AI agents that work for you around the clock.
RAG & Vector Search
Make AI talk to your own data with retrieval-augmented generation.
AI Strategy & ROI
Measure and justify AI investments with frameworks that actually work.
AI Ethics & Hallucinations
Know when AI is making stuff up - and what to do about it.
AI Governance & Scaling
Deploy AI across your org without losing control of it.
Prompt Engineering 201
Advanced prompting patterns that 10x your AI output quality.
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