Build spec for a decision tool that asks clarifying questions, then runs six analytical frames such as expected value and regret minimization on a user's choice.
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Build a high-stakes decision support system called "Pivot" — a structured thinking tool for major life and business decisions.
This is distinct from a simple pros/cons list. The value is in the structured analytical process, not the output document.
Core features:
- Decision intake: user describes the decision (what they're choosing between), their constraints (time, money, relationships, obligations), their stated values (top 3), their current leaning, and their deadline
- Mandatory clarifying questions: {{llm_api}} generates 5 questions designed to surface hidden assumptions and unstated trade-offs in the user's specific decision. User must answer all 5 before proceeding. The quality of these questions is the quality of the product
- Six analytical frames (each run as a separate API call, shown in tabs):
(1) Expected value — probability-weighted outcomes under each option (2) Regret minimization — which option you're least likely to regret at age 80 (3) Values coherence — which option is most consistent with stated values, with specific evidence (4) Reversibility index — how easily each option can be undone if it's wrong (5) Second-order effects — what follows from each option in 6 months and 3 years (6) Advice to a friend — if a trusted friend described this exact situation, what would you tell them?
- Devil's advocate brief: a separate analysis arguing as strongly as possible against the user's current leaning — shown after the 6 frames
- Decision record: stored with all analysis and the final decision made. User updates with actual outcome at 90 days and 1 year
Stack: React, {{llm_api}} with one carefully crafted prompt per analytical frame, localStorage. Focused, serious design — no gamification, no encouragement. This handles real decisions.构建一个名为“Pivot”的高风险决策支持系统——一种用于重大人生和商业决策的结构化思维工具。
这与简单的利弊清单不同。其价值在于结构化的分析过程，而非输出的文档本身。
核心功能：
- 决策输入：用户描述所面临的决策（他们正在权衡的选择）、约束条件（时间、金钱、人际关系、义务）、声明的价值观（前3项）、当前倾向以及截止日期
- 强制性澄清问题：{{llm_api}} 生成5个问题，旨在揭示用户特定决策中隐藏的假设和未言明的权衡。用户必须回答全部5个问题后才能继续。这些问题的质量即产品的质量
- 六种分析框架（每种作为单独的 API 调用运行，以标签页形式展示）：
(1) 期望值 —— 在每种选项下的概率加权结果 (2) 后悔最小化 —— 到80岁时最不可能后悔的选择 (3) 价值观一致性 —— 哪个选项与声明的价值观最一致，并提供具体证据 (4) 可逆性指数 —— 每个选项在出错时能多容易被撤销 (5) 二阶效应 —— 每个选项在6个月和3年后会引发什么后果 (6) 给朋友的建议 —— 如果一位可信的朋友描述了完全相同的情况，你会告诉他们什么？
- 反方论点简报：一份独立的分析，尽可能有力地反对用户的当前倾向——在六个分析框架之后显示
- 决策记录：保存所有分析及最终做出的决策。用户需在90天和1年后更新实际结果
技术栈：React、{{llm_api}}（每个分析框架对应一个精心设计的提示词）、localStorage。专注而严肃的设计——无游戏化元素，无鼓励性语言。该系统处理真实决策。