Recruiting ✦ 82outlineNot a hiring decision engine

简历 / 招聘筛选

结构化简历 + 岗位要求;Score/Choice;置信度门控。非录用引擎。

Who: Recruiting ops / ATS integrators. Steps: (1) State = resume JSON + role must-haves. (2) Score/Choice per requirement; Noul for red flags. (3) Code ranks / gates advance; low confidence → human. Expected effect: consistent first-pass filter. Caveat: not a hiring decision engine; no protected-class scoring.

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
Audience: recruiting ops / ATS.
Steps:
1) Parse resume into structured state + role must-haves.
2) Per requirement: Score/Choice; Noul red_flags.
3) Code ranks candidates; low confidence → human review.
Expected effect: consistent first pass.
Ban: protected-class or demographic scoring.

需要授权:typesafe-sdkats

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。

Keep high-stakes decisions with humans. Sources: TypeSafe use-case map · recruiting cluster notes in awesome-jev-usecases · community CV entries in cobanov/awesome-jev (label community).

Prefer must-have Scores for hard gates (degree, years, location policy). Keep protected-class attributes out of prompts and scoring inputs; document legal review. Thresholds are not universal — label any published numbers as author-reported.

Sources (wave-2 deepen)

为什么它好用

Same closed-label + confidence pattern as triage, applied to must-have checks.

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