Topics
Four in-depth guides. Each one is a complete treatment of its subject, and each one collects every article, lesson, tool and definition we have written on it. Start wherever your question is.
Understand what AI is doing well enough to predict when it will fail you.
A plain-language explanation of how AI language models actually work — prediction, tokens, context, training, and retrieval — and why the mechanism explains every strength and every failure.
Budget for AI without being surprised by the bill or oversold on the savings.
What AI actually costs a business: how token billing works, why real bills exceed estimates, subscriptions versus API, and the costs that never appear on any price list.
Know what is genuinely risky about AI and what is just noise.
What happens to your data when you use AI, whether providers train on it, when to trust the output, and how to set rules your team will actually follow.
Pick the right first project and get it past the demo stage.
A practical guide to applying AI in a small business: which processes are good candidates, what stays human, how to avoid the pilot that never becomes a process, and what to do first.