The answer is only as good as what you told it. Learn what context actually is to a model, how much is enough, why long chats drift — and how to write yourself down once so every tool starts the day knowing you.
The model has read nearly everything humanity ever published — and it still knows less about your Tuesday than someone you met last week does. Every disappointing answer you've ever gotten lives in that gap.
This course teaches the skill underneath every AI tool you'll ever use: supplying the situation the model can't see. You'll learn to read a bad answer as a diagnostic instead of a verdict, what context is actually made of (six raw materials, in two families), why more context can make output worse, why long conversations drift away from things you clearly said, and how a one-page context doc — written once, pasted anywhere — ends the daily ritual of re-introducing yourself to software.
It's tool-agnostic on purpose. Examples name ChatGPT, Claude, Gemini, and Perplexity, but the skill transfers to every one of them, including the ones that don't exist yet. The final lesson draws the honest boundary: the failures context can't fix, and how to spot them in under a minute.
Regular AI users: you get usable results sometimes, generic ones often, and you're done blaming the model without knowing what to change.
Founders and operators: you hand real work to AI daily and want first drafts that sound like your business, not like the average of the internet.
Anyone who briefs anyone: the craft of stating your situation clearly turns out to transfer — to contractors, colleagues, and every machine that asks "what do you need?"
