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I spent three weeks debugging an AI chatbot. It kept timing out.
The problem was not the API. The problem was how I called it.
I built a customer support chatbot for a SaaS product. We used an AI API with good accuracy. But in production, everything broke.
Users asked questions and waited. Then they saw a 504 Gateway Timeout. About 15% of requests failed. Even when they worked, the answer arrived in one big chunk after 20 seconds. Users left the chat before the answer finished.
I tried several wrong fixes first:
- I increased the timeout to 60 seconds. This made failures take longer. Users hated it.
- I used synchronous retries. This caused a backlog of requests. My server memory spiked.
- I looked for async task support. The API did not have it.
I almost rolled back to a simple FAQ system. Then I decided to use streaming.
Streaming allows the model to send partial tokens as it generates them. This solved two main issues:
- Perceived latency dropped to near zero. The first piece of text arrived in 200ms.
- Timeouts became manageable. I could show the user what we had even if the connection broke.
To make this work, I built a robust retry mechanism. Here is my process:
- Open a streaming connection using aiohttp in Python.
- Read chunks as they arrive and show them to the user.
- Track the last token position.
- If the connection drops, wait a short time and reconnect.
- Use a resume parameter to continue where we left off.
- Cap total attempts at 3.
After I deployed this, timeout errors dropped from 15% to less than 0.5%.
Streaming is not a perfect solution. You must consider these points:
- Check if your API supports streaming first.
- Streaming adds code complexity.
- Managing many streams can use more memory.
- You must handle user interruptions like backspaces gracefully.
Do not use streaming if your API responses are always under 2 seconds. Do not use it for offline batch processing.
Lessons I learned:
- Read the API docs carefully. I skipped the streaming section before.
- Add monitoring early. I had no data on latency until users complained.
- Design for failure. Every network call must handle partial errors.
- Use existing libraries. Packages like httpx handle streaming and retries well.
How do you handle unreliable AI responses in your products?
Source: https://dev.to/__c1b9e06dc90a7e0a676b/how-i-fixed-my-ai-chatbots-timeout-nightmare-19md