Some good best practices here on AI token cost optimization. None of these happens though without a deep understanding of the underlying work being done in a non-abstract way. The ultimate implication is that a layer between the work itself and the underlying intelligence needs to deeply understand your workflows, context, and business process. Now, each individual company doing this on their own is unlikely to be effective at scale, so as a consequence, this is effectively the playbook for any applied AI company right now. By evaling the models for the applied use cases, deeply understanding the domain, having tuned UX and features for the use case, and having the ability to support adoption and change (via FDEs), allow this layer to add a ton of value. And as a result, enterprises get higher ROI because you actually can get *more* intelligence per dollar by having optimal architecture and workflows. There will be many horizontal and vertical versions of this approach. Huge opportunity right now.
这里总结了一些关于 AI token 成本优化的很好的最佳实践。不过，如果不能以一种非抽象的方式深刻理解底层正在进行的工作，这些做法都无从实现。其最终含义是：位于实际工作与底层 intelligence 之间的那一层，必须深度理解你的工作流、上下文和业务流程。现在，如果每家公司都各自单独来做这件事，规模化后不太可能有效，因此，这实际上就是当下任何 applied AI 公司的行动手册。通过针对实际应用场景对模型进行 eval，深度理解领域知识，为该 use case 打磨好 UX 和功能，并具备支持采用与变更的能力（通过 FDEs），这一层就能创造大量价值。结果是，企业能够获得更高的 ROI，因为通过最优的架构和工作流，你实际上可以用每一美元换来*更多*的 intelligence。这种方法会有很多 horizontal 和 vertical 的版本。现在是巨大的机会。