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LLM response generation explained: Tokenize, Prefill, Decode, Stream

An LLM's response generation process can be understood as a "Word Factory" with four key machines: the Chopper (tokenize), the Reader (prefill), the Typewriter (decode), and the Delivery Tape (stream). The Chopper converts text into numerical tokens, which the Reader processes simultaneously, storing notes in a KV cache. The Typewriter then generates the answer token by token, as each new token depends on the preceding ones, utilizing the KV cache to avoid re-processing the entire input. Finally, the Delivery Tape streams each generated token to the user as it's produced, creating the effect of real-time typing. AI

IMPACT Explains the internal workings of LLM response generation, clarifying the steps involved in producing text.

RANK_REASON The item explains the technical process of LLM response generation using an analogy.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM response generation explained: Tokenize, Prefill, Decode, Stream

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The item explains the technical process of LLM response generation using an analogy.
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  1. dev.to — LLM tag TIER_1 English(EN) · Sachin Kr. Rajput ·

    One Request, End to End: Tokenize, Prefill, Decode, Stream

    <p>You type <strong>"Why is the sky blue?"</strong> and press Enter.</p> <p>There's a short pause. Then words start appearing, one after another, like someone is typing to you very fast.</p> <p>What happened during that pause? And why do the words come out one at a time instead o…