How Does an LLM Request and Response Cycle Work? A Full Walkthrough

18 hours ago

The LLM response process begins with the client building a structured JSON request, which passes through an API gateway for validation and routing.
Input text is tokenized into numeric IDs, then assembled into the model's context window with positional encodings.
The model runs a forward pass through transformer layers, using self-attention and feed-forward processing to predict the next token one at a time.
Token-by-token sampling continues autoregressively until an end-of-sequence token is produced, with parameters like temperature controlling randomness.
Streaming (via Server-Sent Events) pushes each token to the client as it's generated, enabling word-by-word display.
On the client side, tokens are detokenized back into readable text and rendered incrementally.
Key latency metrics are Time to First Token (TTFT) and inter-token latency, which together shape user experience.

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