Transformer Model
A transformer is the neural-network architecture behind current large language models: it reads a whole sequence of tokens at once and uses a mechanism called attention to weigh how much each token should shape the reading of every other. The models behind ChatGPT, Claude, Gemini and Llama are all transformers.
Attention, without the algebra
Earlier sequence models read text one token at a time and carried a summary forward, so by the end of a long sentence the beginning had faded. A transformer instead lets every position look at every other position directly. For each token the network computes how relevant each other token is to it, weights them accordingly and mixes their representations into a new one. Stacking many such layers lets the model build up meaning that depends on relationships across the whole passage: which noun a pronoun refers to, which business a price belongs to, which claim a caveat qualifies. Because the positions are processed together rather than in turn, training runs in parallel on large hardware, which is what made models of this size practical.
What follows for a page a model reads
Attention operates inside the context window, on the tokens actually present. A fact and its subject are connected only if both are there and the pattern between them is clear. A sentence that puts the business name, the service and the specific claim together gives the model a short attention path from claim to owner. A claim on one page and the name on another, or a fact buried under a heading that asks a different question, gives it a long one. The architecture rewards proximity, which is a plain writing instruction as much as a technical fact.
A common question
How should a page be written, given how attention links a fact to its subject?
Keep them close together. Attention works only over the tokens actually present in the window, and it ties a fact to its owner when both are there and the pattern between them is plain. A sentence holding the business name, the service and the specific claim leaves the model a short path from claim to owner. Splitting the claim onto one page and the name onto another, or burying a fact under a heading that asks something else, makes that path long.