Understand parameters, loss, and why a good training score is not enough.
3 min read
The short version
A model learns by guessing, being told how wrong it was, and adjusting, over millions of examples. After that it is fixed. Showing it your document in a chat does not teach it anything permanent, and knowing how it learned explains why it can be good at patterns and poor at exact facts.
The idea
A neural network is a model made of linked layers of maths. Training adjusts its stored numbers. The system tries a task, measures an error, and uses that feedback to adjust those numbers. Repeating this process helps it learn patterns.
What to check
Training is different from reading a file in a chat. A file can affect the next answer without changing the model's stored numbers. The application may also save files or memories. Check its terms and settings; do not assume the file vanishes when the chat ends.
A model can do well on examples it has seen and poorly on new ones. A useful test uses fresh examples that did not guide the setup.
Examples are teaching material. For legal rules, read the current primary text linked below. A revision date does not certify every claim.
Sources and further reading
Linked sources support the technical background identified below. The legal workflows, prompts, and fictional examples are original editorial exercises; they are not claims of endorsement by the linked authors.
