Understand AI
How it works behind the scenes. For judges, lawyers, and curious newcomers: what a model does, what it can see, why a persuasive answer can be wrong, and what courts and regulators already expect. No code.
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.
What a prompt is, including the part you never see
A prompt is everything the model reads before it answers, including hidden instructions the application adds. Treat all of it as visible, and write explicit goals, source boundaries, and output requirements.
Verify a work product before relying on it
Review source support and completeness in both directions.
What courts expect when you use AI
Check the rule, verify the source, and keep a record of the review.
Reading AI-assisted material from the bench
For judges and those who appear before them: what to look for, what to ask, and how courts themselves are using AI.