Calling LLM APIs: An OpenAI Example End-to-End, Including Streaming
~14 min read
Putting the whole unit together: authenticate, send a JSON request with your prompt, check the status code, parse the JSON response — and handle the streaming variant that most chat UIs actually use.
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Key points
- •Calling an LLM API combines everything in this unit: POST request, Bearer token auth header, JSON body with your prompt/settings, status-code check, JSON response
- •Check the status code before trusting the response body — a non-2xx means the generated text is missing and the error is in the body instead
- •Streaming (stream: true) keeps the connection open and sends small chunks as they're generated, instead of one big response after a long wait
- •Streaming is what produces the familiar 'typing' effect in chat UIs, and it directly improves perceived responsiveness (time to first token)
- •LLM APIs commonly hit 429 rate-limit errors under load — production code typically retries with exponential backoff rather than failing immediately