📄 中文摘要
在优化 AI 应用的过程中，提示缓存和一般缓存技术被广泛讨论，它们能够有效降低成本和延迟。然而，即使是经过全面优化的 AI 应用，有时生成响应仍需一定时间。响应流式传输技术可以显著改善用户体验，通过实时传输部分响应，使用户能够更快地获取信息。这种方法不仅提升了应用的互动性，还能在一定程度上缓解用户等待的焦虑，从而提高整体满意度。实施响应流式传输需要对系统架构进行适当调整，以确保数据流的高效处理和传输。
如何通过响应流式传输提升 AI 应用的速度和交互性
📄 English Summary
How to Make Your AI App Faster and More Interactive with Response Streaming
Optimizing AI applications often involves discussions around prompt caching and general caching techniques, which can effectively reduce costs and latency. However, even fully optimized AI applications may still take time to generate responses. Response streaming technology can significantly enhance user experience by delivering parts of the response in real-time, allowing users to access information more quickly. This approach not only improves the interactivity of the application but also alleviates user anxiety associated with waiting, thereby increasing overall satisfaction. Implementing response streaming requires appropriate adjustments to system architecture to ensure efficient data flow and transmission.