📄 中文摘要
在构建基于大型语言模型（LLM）的 AI 系统时，常见的直觉是让 LLM 决定所有事情。然而，这种做法存在延迟、成本和不确定性等三个致命问题。每次接收到用户消息时，LLM 需要花费一定时间进行分类和生成响应，导致用户体验不佳。此外，LLM 的路由过程是概率性的，可能导致不一致的分类结果，从而产生难以调试的错误。为了解决这些问题，反射路由设计模式应运而生，它借鉴了人类神经系统的概念，提供了一种确定性的智能分流方法，能够有效降低延迟和不确定性。
为什么你的 AI 系统不需要用 LLM 来做路由？反射路由设计模式解析
📄 English Summary
為什麼你的 AI 系統不需要用 LLM 來做路由？反射路由設計模式解析 | Why Your AI System Doesn't Need an LLM for Routing
Building AI systems based on Large Language Models (LLMs) often leads to the instinct of letting the LLM decide everything. However, this approach has three critical flaws: latency, cost, and non-determinism. Each time a user message is received, the LLM takes time to classify and generate a response, resulting in a poor user experience. Furthermore, the probabilistic nature of LLM routing can lead to inconsistent classifications, causing hard-to-debug errors. To address these issues, the Reflex Routing design pattern emerges, borrowing concepts from the human nervous system to provide a deterministic intelligent dispatch method that effectively reduces latency and uncertainty.