Streaming (LLM)
Definition
LLM streaming delivers model output token-by-token as it's generated rather than waiting for the complete response, significantly improving perceived latency and user experience in conversational AI applications.
Why It Matters
A typical LLM response takes 3-15 seconds to generate completely. Without streaming, users stare at a blank screen until the full response arrives. With streaming, they see text appear progressively, word by word, making the same 10-second generation feel instantaneous.
This isn’t just perception. Streaming enables new interaction patterns. Users can start reading early parts of a response while later sections generate. They can interrupt generation if the response goes off-track. Chat interfaces feel conversational rather than request-response transactional.
For AI engineers, implementing streaming correctly is essential for production applications. Users expect the ChatGPT-like progressive text experience. Applications that block until complete generation feel slow and unresponsive by comparison, regardless of actual latency.
Implementation Basics
Streaming implementations involve handling server-sent events:
Server-Sent Events (SSE) is the common transport. The server sends a stream of events, each containing a chunk of generated text. The client reads events as they arrive and updates the display.
API configuration enables streaming. With OpenAI, set stream=True and iterate over the response. With Anthropic, enable streaming and handle message delta events. Each provider has slightly different event formats.
Frontend handling accumulates chunks. JavaScript receives events via EventSource or fetch streaming. Append each chunk to the displayed text. Handle the final event that signals generation is complete.
Error handling becomes more complex. Errors can occur mid-stream after partial content is already displayed. Implement graceful degradation, show what was generated and indicate the interruption.
Start with a simple streaming endpoint using FastAPI’s StreamingResponse. Test with curl before building frontend integration. Handle backpressure if clients disconnect mid-stream. Add client-side error recovery for dropped connections. The complexity is worth it. Streaming transforms user experience from “waiting for AI” to “talking with AI.”
Source
Streaming allows partial progress for long requests by sending events as the model generates tokens, enabling progressive display of results.
https://platform.openai.com/docs/api-reference/streaming