LemonLime 如何通过单次提示自动化现有工作流——自学习与自由描述两种机制详解

@fberrez1 这里发生了两件事，我把它们都分享出来。

首先，它会从现有工具中能获取的上下文来推断工作流。

例如，假设在你的业务完成一个客户时，大约有5封邮件来回收发。其中某处有一份书面提案，之后你还附带了一条宣布合作/销售的社交媒体提及。

一旦你连接好工具，LemonLime 就会察觉到这种模式，而且连接的工具越多，其识别强度和准确性就越高（部分原因是它能更好地区分重复模式与一次性工作）。因此，当下一个销售线索进来时，LemonLime 基本上会提议：“以下是销售流程中通常发生的事，我已经了解你的语言风格、我们应该推动的目标以及提案中要包含的内容。”然后，如果一切看起来没问题，你只需点击一个按钮，它就会执行到底。这就是自我学习、自我创建的一面。

对于希望获得更多控制权，或者想构建尚不存在、未被 LemonLime 发现的新自动化/流程的用户，自由形式描述就出现了。LemonLime 并不是单纯进行推断（这样确实可能杂乱无章），而是能识别主观偏好与可衡量结果之间的区别，并据此进行规划。这意味着，对于更 主观 的事情，它实际上会回头询问用户的意见，或者展示选项供用户选择。而对于更 客观 的事情（例如“我们对这些策略做了 A/B 测试，选项 B 表现最佳”），它会做出更优的决策，并在适用的情况下将该决策先交给你审批。这并非“推断”，而是获得 实际 答案。

@fberrez1 There's two things that happen here, so I'll share both.

First, it's inferring the workflow from the context it's able to gather from your existing tools.

For example, let's say there's around 5 emails that go back and forth when your business closes a customer. Somewhere in there, there's a written proposal, and somewhere past that, you include a social media mention announcing the partnership/sale.

Once you've connected your tools, LemonLime is going to pick up on this pattern, with greater strength and accuracy the more tools connected (partially because it's better able to differentiate between repeat patterns and one-off work). So, the next time a sales lead comes in, LemonLime is basically going to propose "here's what normally happens in a sales flow, I already know your language, what we should be pushing for, and what to include in the proposal." Then, if everything looks good, you can literally click a single button and it'll follow through. This is the self-learning, self-creating side.

For people looking for more control or building new automations/flows that don't already exist or haven't been surfaced by LemonLime , that's where the freeform description comes from. Instead of inferring (which, you're right, can be messy), LemonLime recognizes the difference between taste and measured result, and plans around that. What that means is things that are more subjective will actually come back to the user asking for input, or it'll show them options to choose from. Things that are more objective ("we A/B tested these strategies, and option B is performing best") it's going to make the more optimal decision, and where applicable bounce that decision to you for approval first. Instead of "inferring", it's getting the actual answer.