Implementing Streaming Responses: Why It Matters and How to Do It

Waiting 10 seconds for AI responses feels broken. Watching text appear progressively feels fast, even if total time is similar. Streaming responses are essential for production AI applications, but implementation has pitfalls.

Server-Sent Events (SSE) is the simplest approach. Your server maintains a connection and sends chunks as they arrive from the AI API. Most AI providers support streaming natively. The frontend just needs to handle incremental updates.

The complications arise in processing. If you need to validate, transform, or augment AI output, streaming requires buffer management. You can't check for harmful content until you've seen the full response, but users expect to see something immediately.

Error handling is different for streams. A connection drop mid-response requires graceful degradation—show what you have, offer retry, don't lose context. Rate limits mid-stream are particularly annoying to handle.