Large Language Model

A large language model is trained on an enormous corpus of text with a deceptively simple objective: given some text, predict the next token. Do that at sufficient scale and something surprising emerges — to predict text well, the model has to encode grammar, facts, styles, reasoning patterns and a great deal of implicit world knowledge.

The result is a general-purpose text engine. The same model can summarize a contract, draft an email, explain a codebase or argue a position, because all of those are text-prediction problems in disguise. Modern models extend this to images, audio and video, at which point they are usually called multimodal.

The next-token framing also explains the failure modes. The model is optimizing for plausible continuations, not for truth, which is why a confidently wrong answer looks exactly like a correct one.

Nearly every AI product you encounter is a language model with an interface and some plumbing around it. Understanding that they predict rather than retrieve explains most of their behavior: why they are fluent but sometimes wrong, why phrasing a request differently changes the answer, and why giving them the right source material up front matters more than asking the question more forcefully.

The practical skill is supplying context. A model asked to answer from its training alone is guessing from memory; the same model given the relevant document usually answers well. That gap is the entire reason retrieval-augmented generation exists.