How Does an LLM Request and Response Cycle Work? A Full Walkthrough
14 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.