Patterns ✦ 53outlineNot a production recipe — calibrate thresholds

ML features from Jev questions

适用:ML eng。效果:Semantic features + Autoresearch loop

Who: ML eng. Steps: Free text → many Noul/Score → vectors for classical model. Expected effect: Semantic features + Autoresearch loop.

1

复制提示词(保留英文原文效果最好)。

2

在代码里实现——调用 System One / Jev,由你的程序组合答案。

3

回答它的设置问题——大多数提示词会先采访你,然后进入固定节奏。

提示词

提示词
# ML features from Jev questions
State: relevant software state for this pattern.
Ask: Choice / Score / Noul questions that close the decision space.
Then: compose the route or action in code; escalate when confidence is low.

需要授权:typesafe-sdk

它是怎么搭起来的

以下说明为英文原文(来自社区作者),提示词本身建议保留英文。

Steps

  1. Free text.
  2. many Noul/Score.
  3. vectors for classical model.

Sources

Metrics and demo claims are author-reported or cookbook-reported unless you measure them yourself. Calibrate on your data.

为什么它好用

Semantic features + Autoresearch loop

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