Streaming Responses to Your Interface
Stream model output token by token from the API through your backend to the browser, errors included.
A taste of a lesson
Streaming works locally but in production the whole answer appears at once. Why?
That pattern almost always means something between your backend and the browser is buffering the response, usually a reverse proxy, load balancer or hosting layer that waits to collect the full body. Check three things: that your backend flushes after each chunk, that the proxy has response buffering disabled for this route, and that compression is not holding chunks back. Server sent events should also send the right content type. Which proxy or hosting setup sits in front of your app?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Read streamed deltas and the final stop reason and usage event
- Relay a stream from your backend to the browser without exposing keys
- Render partial text smoothly and finalise it on completion
- Handle mid stream errors, user cancellation and buffering proxies
- Decide how to stream when you need structured output or tool calls
Lesson plan
- 1 What streaming changes Explain time to first token versus total time and when streaming helps. Start
- 2 Reading a stream from the API Consume streamed events and assemble the full answer and usage. Start
- 3 Relaying through your backend Pass a stream from the model API to the browser via your own endpoint. Start
- 4 Rendering partial text Display arriving text smoothly, including markdown and code. Start
- 5 Errors, stops and buffering Handle failures, cancellation and infrastructure that breaks streaming. Start
- 6 Streaming structured output and tools Choose an approach when the streamed output must be parsed. Start
Try asking
About this tutor
For developers who have a working request and response flow and want answers to appear as they are written, the way chat products do. You learn how streaming APIs deliver events, how to read deltas and the final usage report, how to relay a stream from your backend to a web page safely, and how to render partial text without flicker. The tutor covers the hard parts: errors halfway through, users pressing stop, proxies that buffer, partial JSON when you need structured output, and why streaming improves perceived speed rather than total time.
Reviews
4.5
2 ratingsSample
- Benedikt R.Sample
The timeline explanation of first token versus total time helped me argue for streaming with my team. Proxy buffering was exactly my production bug.
- Hye-jin P.Sample
Good coverage of stop buttons and mid stream errors, which tutorials usually skip. The structured output part was short but honest.
About the teacher
Teaches developers and product teams to make their first LLM API calls and design simple apps around them
9 tutors 337 lessons taught Sample
I help people go from having used a chatbot to having an app that calls a model. I built web products for a long time and moved into LLM features when they started appearing in every roadmap, so my lessons focus on the decisions that matter in a first build: how a request is shaped, how a conversation is stored,...
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