AI essentials
Understand what a model does, what it can see, and why a persuasive answer still needs support.
Explain an AI answer without confusing fluency, access, and verification.
What actually happens when you ask AI a question?
A language model predicts text one piece at a time; the application around it decides what it sees. Knowing which part did what is how you check an answer.
Tokens, attention, and why wording matters
Models read tokens, not words. Legal citations can use more tokens than ordinary prose, and clear labels help the model link the right pieces.
What uploading a document does—and does not—establish
The context window is the model's whole working desk. Learn what fills it, what happens when it overflows, and why fitting is not the same as being read.
Why a persuasive answer can still be wrong
Recognize the kinds of errors that polished writing can hide.
How models learn from examples
Understand parameters, loss, and why a good training score is not enough.
A short history of language models
From counting word pairs to transformers and chat assistants: each step explains a strength or weakness you still see today.
Why the same question can produce different answers
Understand generation settings and evaluate reasoning by its support.
A useful way to work
Read in order for a gradual introduction, or use an individual lesson as a reference when a question comes up.