组合评分

组合评分

把复杂的判断拆成原子 score，再用代码里你掌控的权重合成。

我们常常要同时按几个判定标准给一组条目排序。组合评分是一种很好想的做法：把这次判断拆成相互独立的维度，每个维度单独打分，再用代码里你掌控的权重把它们合起来。

示例：简历筛选

设想你在处理工程岗位的简历。你想按几个判定标准给候选人排序，最终选出前 X 名进入下一轮。

%%{init: {"fontFamily": "Inter, sans-serif", "flowchart": {"rankSpacing": 35, "wrappingWidth": 300, "subGraphTitleMargin": {"top": 12, "bottom": 36}}}}%%
flowchart LR
    resume["candidate resume"]

    subgraph req["TypeSafe evaluates questions<br/>in parallel"]
        direction TB
        py["<b>Score:</b> Python depth"]
        lead["<b>Score:</b> team leadership"]
        arch["<b>Score:</b> system design"]
        general["<b>Score:</b> generalist"]
        %% Invisible links stack the questions; they are answered in parallel.
        py ~~~ lead ~~~ arch ~~~ general
    end

    resume -- "one request<br/>resume + 4 questions" --> req
    req -- "one response<br/>4 score answers" --> normalize["<b>normalize scores to 0–1</b><br/>divide each by 4 in your code"]
    normalize --> ic["<b>senior IC weights</b><br/>40% Python + 10% leadership<br/>40% design + 10% generalist"]
    normalize --> em["<b>engineering manager weights</b><br/>15% Python + 40% leadership<br/>20% design + 25% generalist"]
    ic --> rank["rank candidates<br/>for each role"]
    em --> rank

第 1 步：给每个维度单独打分

{
  "python_depth": {
    "type": "score",
    "instructions": "How much depth of python experience does this candidate have, based on the supplied resume?",
    "criteria": [
      "No Python experience mentioned",
      "Mentioned but no detail",
      "Used in projects, some specifics",
      "Primary language, multiple projects",
      "Deep expertise: architecture, performance, libraries"
    ]
  },
  "team_leadership": {
    "type": "score",
    "instructions": "How much experience does this candidate have managing or leading engineering teams?",
    "criteria": [
      "No management experience mentioned",
      "Informal mentorship or tech lead role",
      "Led a small team or project",
      "Managed a team with direct reports",
      "Managed multiple teams or an engineering org"
    ]
  },
  "system_design": {
    "type": "score",
    "instructions": "How much experience does this candidate have designing large-scale or distributed systems?",
    "criteria": [
      "No architecture work mentioned",
      "Contributed to design discussions",
      "Designed components of a larger system",
      "Owned architecture of a significant system",
      "Designed systems at scale across multiple domains"
    ]
  },
  "generalist": {
    "type": "score",
    "instructions": "How much evidence is there that this candidate picks up unfamiliar tools, roles, or domains outside their core specialty?",
    "criteria": [
      "Only one domain or role mentioned",
      "Some variety but within a narrow field",
      "Worked across a few different areas or tech stacks",
      "Regularly moved between domains, wore many hats",
      "Track record of ramping up in unfamiliar areas and delivering"
    ]
  }
}

第 2 步：按权重合成

每个维度都先归一化到 0–1，再加权。有了权重，你就能方便地调整各维度的相对重要性，又不会丢掉单个 score 里的任何细节。

scoring.py

py      = response.answers["python_depth"].score / 4
lead    = response.answers["team_leadership"].score / 4
arch    = response.answers["system_design"].score / 4
general = response.answers["generalist"].score / 4

# Senior IC
ic_score = (0.40 * py) + (0.10 * lead) + (0.40 * arch) + (0.10 * general)

# Engineering Manager
em_score = (0.15 * py) + (0.40 * lead) + (0.20 * arch) + (0.25 * general)

这样你就能按合成后的 score 给候选人排序。但更重要的是，最终分数到底是怎么算出来的，你看得一清二楚。如果排在前面的人选不符合你的预期，调一调权重就能找到合适的平衡。