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A model reads your page, decides what it says, and writes the answer somebody else will read.

What these systems do, what governs their output, and which of it decides whether your material survives into an answer.

On this page

What it is
Large language models are the machines that now write the answer above the results.
The problem until now
Their behaviour was described in product terms, so publishers could not tell what decides whether their material survives into an answer.
What you can do from here
You can see what governs the output, what makes a model invent, and which of it your own text still reaches.
Large language models: your text enters the context window, the model reads it and writes the answer
Large language models: How the parts depend on each other.

Try this first

Three tests, about ten minutes. They show what governs the answer a model writes.

Crawlers that fetch JavaScript without running it never see a graph added after load. See Static HTML and LLMs.

Tests on this page you have run: 0 of 3. The count stays in this browser only.

Test: Large language models

  1. Ask a model the same factual question three times, each in a new chat.
  2. Note the answer in your own words.
  3. Think: which parts stayed the same?

What is stable in a generated answer.

Test

  1. Paste your longest page into an assistant and ask for the three most important facts.
  2. Note the answer in your own words.
  3. Think: were the facts you consider important among them?

Which facts survive a long text.

Test

  1. Ask a model where a statement about your field comes from.
  2. Note the answer in your own words.
  3. Think: could you trace it to a source?

What a model remembers, beside what it reads.

Large language models sit between what is published and what a person reads. They select, compress and rewrite. Whatever they cannot restate does not reach anybody.

Subjects in this field

HallucinationThe public meaning is psychiatric. Everything about machines is the gap.Context windowHow much a model holds at once, and why the advertised number misleads.Prompt engineeringShaping what a model does, and the techniques below the visible text.Temperature parameterOne number that decides how varied the output is, and what it really controls.Fine-tuningAdapting a trained model to a purpose, and what it costs in capability.Parameter-efficient fine-tuningAdapting a large model by training a small part, and what that omits.Instruction tuningTeaching a model to follow directions, and what pleasing costs.

The subjects below are the parts of that machinery which bear on published material directly: how much a model can hold, what governs the variety of its output, what makes it invent, and what training does to what it will and will not say.

Each one states what is settled, names what the usual treatment leaves out, and answers the questions that follow.

Last revised 2026-09-16.

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