𝗛𝗼𝘄 𝗜 𝗠𝗲𝘀𝘀𝗲𝗱 𝗨𝗽 𝗔𝗜 𝗦𝘁𝗿𝗲𝗮𝗺𝗶𝗻𝗴 (𝗔𝗻𝗱 𝗛𝗼𝘄 𝗬𝗼𝘂 𝗔𝘃𝗼𝗶𝗱 𝗜𝘁)

𝗛𝗼𝘄 𝗜 𝗠𝗲𝘀𝘀𝗲𝗱 𝗨𝗽 𝗔𝗜 𝗦𝘁𝗿𝗲𝗮𝗺𝗶𝗻𝗴 (𝗔𝗻𝗱 𝗛𝗼𝘄 𝗬𝗼𝘂 𝗔𝘃𝗼𝗶𝗱 𝗜𝘁)

I built a code review assistant. It used AI to give feedback in real time. I wanted a stream of tokens. Everything went wrong.

First, I used a standard REST endpoint. The backend waited for the full AI response. The frontend timed out after 30 seconds. Users complained.

Next, I tried streaming with a buffer. I collected all tokens before sending them. The latency stayed the same. The user still waited.

Then, I tried Server-Sent Events (SSE). I ignored backpressure. The AI pushed tokens too fast. Memory grew. Connections died.

I fixed it with FastAPI and asyncio. Here is the setup:

• The client opens an SSE connection.
• An async task streams tokens from the AI.
• The backend forwards tokens immediately.
• A small buffer manages backpressure.

Streaming is hard. Keep these points in mind:

• Heavy client processing adds lag.
• SSE connections drop on bad networks.
• Use retry logic on the client.
• Use a worker model like uvloop for many connections.
• Handle errors mid-stream with special tokens.

My final advice:

• Start with a non-streaming prototype.
• Use event-driven architecture.
• Test with real network packet loss.

Source: https://dev.to/__c1b9e06dc90a7e0a676b/how-i-messed-up-ai-streaming-and-how-you-can-avoid-it-11h6