In a hybrid architecture, the 智能路由器 can choose between an AI 系统 and a 独立系统. Repetitive, structured, and predictable workloads can be executed by deterministic components such as rules, parsers, PHP, SQL, APIs, or fixed algorithms instead of repeatedly calling an LLM.
AI is then reserved for tasks that genuinely require intelligence , ambiguity, interpretation, reasoning, unfamiliar patterns, or situations where deterministic execution produces low confidence. The 系统 can also escalate difficult cases from a smaller model to a more capable model when necessary.
This creates two levels of routing. The first is execution routing: 独立系统 vs AI. The second is model routing: which AI model should handle the task. This is fundamentally different from conventional multi-model routing, which starts with the assumption that every task must eventually be processed by AI.
The principle is simple: use intelligence only where intelligence is required. Instead of asking only, “Which AI should do this?”, a more efficient architecture first asks, “Should AI do this at all?” This approach can reduce token consumption and inference costs while improving latency, consistency, predictability, and scalability.
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我会把which AI model should handle这一段分享给需要了解技术的同事。
如果有更多use intelligence only where intelligence的数据和结果会更完整。
这篇文章适合团队用来开始讨论parsers, PHP, SQL, APIs。
看第二遍才注意到consistency, predictability, and scalability的细节。
这篇文章把multi-model讲得比一般的AI介绍更具体。
总结部分让unfamiliar patterns, or situations的重点更加清楚。
这篇文章对using a smaller model的解释很清楚，实际操作的重点也很容易理解。 这点我还要再消化一下。
关于repetitive, structured, and predictable的例子很实用，适合团队继续讨论。