How an LLM Turns Your Prompt Into a Response: A Step-by-Step Breakdown

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When a user sends a message to a large language model, the chat app first builds a structured JSON request containing the prompt, system instructions, and parameters before transmitting it over HTTPS. The request then passes through an API gateway that authenticates the user, enforces rate limits, and routes the call to the appropriate compute cluster. Next, a tokenizer converts the input text into numeric tokens, which are fed into the model's context window for processing. The model performs a forward pass through transformer layers, predicting one token at a time until an end-of-sequence signal is reached. These tokens are streamed back to the client in real time and decoded into readable text, producing the characteristic word-by-word appearance of LLM responses.

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