# Response streaming

**Source:** https://promtable.com/glossary/response-streaming

> Response streaming pipes the model's output token-by-token to the client as it's generated, so users see text appearing in real time instead of waiting for the full answer.

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Response streaming pipes the model's output token-by-token to the client as it's generated, so users see text appearing in real time instead of waiting for the full answer.

Streaming is mandatory for any user-facing LLM feature in 2026 — without it the user stares at a spinner for 5-30 seconds. Every major API supports server-sent events (SSE) or websocket streaming. The engineering trade-offs: streaming complicates error handling mid-response, makes structured-output mode (JSON) harder to validate progressively, and prevents the server from inspecting the full output before sending. For tool-use loops, stream the user-facing answer but buffer tool calls until complete.

## When to use

- Any chat or assistant UX.
- Long-form generation where users want immediate feedback.

## Common mistakes

- Streaming JSON without progressive validation — partial JSON breaks parsing.
- No abort/cancel on the client — runaway streams keep generating after the user navigates away.

## Related terms

- [context-window](https://promtable.com/glossary/context-window)
- [agent](https://promtable.com/glossary/agent)
- [system-prompt](https://promtable.com/glossary/system-prompt)

*Last updated: 2026-06-01*
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Original page: https://promtable.com/glossary/response-streaming
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