Explaining LLM

摘要: Search

A typewriter fed by trillions of tokens of books, striking one key at a time, the way an LLM writes

Explaining LLM

An LLM, or large language

正文

A typewriter fed by trillions of tokens of books, striking one key at a time, the way an LLM writes

Explaining LLM

An LLM, or large language model, is a neural network trained on vast text to predict the next token, the engine behind ChatGPT, Gemini, Claude and AI search.

Luis Rijo

Luis Rijo

Luís Rijo has written PPC Land daily since founding it in 2016. Over 10,000 articles, funded by readers, no sponsored coverage. Tips and corrections: luis@ppc.land

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October 13, 2026

9 min read

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large language model

(LLM) is a computer program that has learned to predict the next fragment of text from the fragments before it, and has been trained to do so on a body of writing so large that the prediction becomes useful for drafting, summarising, translating, coding and answering questions. It exists because a single statistical model trained on general text turned out to perform many language tasks that once each needed a purpose-built system. ChatGPT, Google's Gemini, Anthropic's Claude and Meta's Llama are all LLMs, and so are the components that now sit inside search engines, ad platforms and social feeds.

"Large" refers to two quantities: the number of

parameters

, the adjustable numbers inside the model that store what it has learned, and the volume of training text. Neither has an agreed threshold. A model with a few billion parameters counts as "small" today.

How an LLM produces text

The model never sees words. Text is first split into

, chunks of characters drawn from a fixed vocabulary that commonly holds 50,000 to 200,000 entries. In English, one token averages about four characters, so 100 tokens correspond to roughly 75 words, according to OpenAI's help centre. Each token is converted into a long list of numbe

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