AI Engineering · LLM applications that ship
How do you stream LLM output to users?
Short answer
Stream tokens or events from the model to the client as they arrive, render progressively, and handle partial structures and cancellation. Streaming improves perceived latency without reducing total time.
Streaming shows progress while the model generates. For chat this is straightforward text. For structured output or tool calls, partial events need a parser that can handle incomplete JSON. The client also needs a way to stop generation and to reconcile the final message with what was displayed.
- Stream through your backend so keys and policy checks stay server-side.
- Render incremental text but only validate the final structured object.
- Support cancellation and stop billing work the user no longer needs.
- Handle disconnects: persist the completed message or a resume marker.
Follow-ups you should expect
Interviewers use these to check whether the first answer was understanding or memorisation.
- 1How would you stream a tool call safely?
- 2What happens if the connection drops halfway?
- 3Does streaming change your error-handling design?
Say it in the interview
I stream through the backend so API keys and policy checks stay server-side. Text renders as it arrives, and for structured output I show a status while buffering and only display the parsed object after validation. I support cancellation and persist completed messages, because a dropped connection should not lose the answer. Streaming mainly improves perceived latency; it does not make the model faster.
Use this as a model, not a script. Saying it in your own words is what makes it survive follow-up pressure.
Related questions
Reading is step one
Now say it out loud.
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