集里怎么说它

• 《SAP CEO:单靠 LLM 跑不动企业,商业 AI 只差几个月》(03:21起):本集核心论点：审计级任务要 100%（93% 等于不能用），简报类 94-95% 就够；准确率不到 95% 的智能体不发布。

① 提到它的金句

9 条

因此,将这种推理嵌入到我们的搜索智能体中,实际上让我们能够将准确率从大约 50% 提高到了 90。
So embedding this sort of reasoning into our search agent is actually something that got us up from roughly like 50% accuracy all the way to 90.
—— 嘉宾 · [11:53]

指向原始笔记的链接

我想说的是，我见过走错路的地方，是拿着最初的那个 POC——那个展现出巨大前景、比如展现出高准确率的单点解决方案——然后试图从它向上构建，在遇到企业需求时再把它们绑上去。
And I’d say the takeaway is that where I’ve seen it go wrong is taking that initial POC, that point solution that showed so much promise and that showed the high accuracy, for example, and then trying to build up from it, strapping on the enterprise requirements as you come across them.
—— Saul Howard · [17:56]

指向原始笔记的链接

而我见过走得好的地方是，如果你从一开始就认真对待一个可投产的、规模化企业系统的约束，把那些当作你要构建一切的架构原则，然后用你的新原语反向构建，重新达到那个 POC 的准确率。
But where I’ve seen it go well is if you take the constraints of a production-ready scaled enterprise system seriously from the beginning and treat those as the architectural principles that you’re going to build everything upon and then build back up towards that POC accuracy using your new primitives.
—— Saul Howard · [18:32]

指向原始笔记的链接

然后,通过我经历的所有可观测性和评估,我们发现,和一个非常朴素的实现相比,我们的成本下降了 60%,我们的质量和准确率提升了大约 70%。
And then with all the observability and evals that I went through, we saw that against like a very naive implementation, our costs were down by 60% and our quality and our accuracy was up by around 70%.
—— Ameya Ketkar · [10:46]

指向原始笔记的链接

你可以把它理解为一种方式：把你的技能转化成、或者说基于它们创建出非常聚焦、高准确率、非常快、非常便宜的「LM as judge」工具。
You can think of it as a way of turning your skills into or creating off of them very focused, high accuracy, very fast, very cheap LM as judge tools
—— Drew · [29:03]

指向原始笔记的链接

LangChain 一年内重新架构了他们的 OpenDeep Research 三次，还有 Vercel 移除了 80% 的工具，以实现更少的步骤、更快的响应和更好的准确性。
LangChain has re-architected their OpenDeep research three times a year, and then also Vercel has removed 80% of their tools to achieve fewer steps, faster responses, and better accuracy.
—— Philipp Schmid · [15:54]

指向原始笔记的链接

有我所谓的高准确性区间,一些机构基本需要 99 到接近 100% 的准确性,因为错误提取的负面后果就是你完全搞砸你的财务模型。
There’s what I call like the high accuracy regime where like you know some institutions basically need like 99 to almost 100% accuracy because basically the downside of incorrect extraction is you completely mess up your financial model.
—— Jerry Liu · [15:13]

指向原始笔记的链接

AI 理解业务,但有时你需要 100% 的准确率,90% 是不够的。
AI understands business, but sometimes you need an accuracy of 100%, 90% accuracy is not enough.
—— Christian Klein · [05:36]

指向原始笔记的链接

你知道，同样是 0% 的准确率，但你永远不会得到「把脸在键盘上乱按」这种情况，因为优化中的这种不对称性。
You know, it’s the same 0% accuracy, but you never get the mashing your face on the keyboard because of the asymmetry in the optimization.
—— Diogo Almeida · [07:53]

指向原始笔记的链接

② 出现在这些集

1 集

• 《SAP CEO:单靠 LLM 跑不动企业,商业 AI 只差几个月》 — 作为概念

③ 关联

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