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An autoregressive large language model generates text by processing a tokenized prompt, assigning scores to possible next tokens, selecting one, and repeating that cycle. The shorthand “it predicts the next word” is close, but incomplete: tokens are not always words, selection depends on decoding, and an assistant app may add retrieved information or tools around the model.
What happens after you send a prompt to an LLM?
- The system prepares context. The model receives your prompt and any conversation context that fits. In a deployed assistant, surrounding software may also supply instructions, retrieved material, tool results, or filters; those additions are part of the application, not necessarily the model’s next-token operation. OpenAI’s tools guide describes how tools can be used by an assistant.
- A tokenizer splits the text into tokens. A token may be a whole word, part of a word, or punctuation. The boundaries depend on the tokenizer and model, so “one token at a time” does not mean “one word at a time.” OpenAI’s rough estimate for English is about four characters or three-quarters of a word per token, but actual counts vary by encoding and language. OpenAI explains tokens and counting.
- The network processes the available context. In Transformer models, layers update token representations. Self-attention lets the model use relationships among tokens in the context when interpreting what comes next. This is a high-level account of a common architecture, not a description of every language model. The Transformer paper introduced the architecture and its attention mechanism.
- The model scores possible continuations. It produces scores or a probability distribution for candidate next tokens given the current context. A decoding procedure then selects a token. Depending on the method and settings, the system may choose the highest-scoring option or sample among plausible options; it is inaccurate to say every system always picks the single most likely token. OpenAI’s text-generation guide discusses generation and decoding.
- The cycle repeats until generation stops. The selected token is appended to the context, and the model scores the next token again. Generation ends when a stop condition is reached or the output limit is met. The prompt and generated output must fit within the model’s context limit, which varies by model. The text-generation guide covers these generation controls.
Does an LLM predict one word at a time?
Usually, “predict the next word” is an accessible shorthand for the repeated next-token process used by common autoregressive text generators. It leaves out three important details: a token can be only part of a word, the model uses the broader available context to score continuations, and a decoding rule determines how the next token is selected. It also does not explain everything learned during training or every capability of an assistant application.
What is a token in an LLM?
A token is a unit produced by a tokenizer, not a universal word-sized block. For example, common words may map to one token, while a rarer or longer word may be split into several pieces. Punctuation and spaces can also be represented as tokens. That is why token counts are only rough proxies for word or character counts. OpenAI’s estimate—about four English characters or 0.75 words per token—is an approximation, not a fixed conversion. See OpenAI’s token overview.
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The model’s scores for candidate continuations are distinct from the decoding procedure that turns those scores into output. A deterministic method can make the same choice under the same conditions, while sampling-based decoding can select different plausible continuations. The exact behavior depends on the system’s generation settings; the model does not necessarily emit its top-scoring token at every step. OpenAI’s generation documentation describes the role of sampling and related controls.
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Where does the language model end and the assistant begin?
The next-token loop describes a common model-generation process, but a user-facing assistant can be a larger software system. It may gather relevant material, call a tool, or apply instructions and filters before presenting a response. When tools are involved, the assistant can use their results as additional context; that behavior should not be mistaken for the model independently retrieving facts from a lookup table. OpenAI’s tool documentation explains this application-level layer.
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An autoregressive LLM tokenizes the prompt and processes the available context through its network. It scores possible next tokens, and a decoding rule selects one. The selected token is added to the context, and the cycle repeats until a stop condition or output limit is reached. In a deployed assistant, retrieval and tools may also contribute to the final response.
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