From 89ff8ebc39a2f9d35009555c129c380e2092c333 Mon Sep 17 00:00:00 2001 From: promptadmin Date: Sat, 6 Jun 2026 20:41:28 +0000 Subject: [PATCH] Automated ingestion of prompt: High-Stakes Decision Support System --- ...high_stakes_decision_support_system_1535.md | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) create mode 100644 prompts/general/high_stakes_decision_support_system_1535.md diff --git a/prompts/general/high_stakes_decision_support_system_1535.md b/prompts/general/high_stakes_decision_support_system_1535.md new file mode 100644 index 0000000..aa6b5c3 --- /dev/null +++ b/prompts/general/high_stakes_decision_support_system_1535.md @@ -0,0 +1,18 @@ +--- +title: "High-Stakes Decision Support System" +contributor: "@mmanisaligil" +tags: #general, #mmanisaligil +--- + +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. +