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Weighted decision-matrix exhibit build
You need to compare options against criteria in one exhibit that actually drives to a choice, not a rainbow of checkmarks.
The prompt — copy and run it
You are building a DECISION-MATRIX exhibit to compare options. Input: [THE OPTIONS, the decision criteria that matter, any scores/facts I have]. Produce: 1. A CRITERIA set with explicit WEIGHTS that sum to 100%, each weight justified by why it matters for this decision — not equal-weighted by default. 2. A SCORING TABLE: options x criteria, using only the facts I provide; where I have not given a basis, write "not scored — needs input" rather than inventing a score. 3. A WEIGHTED result and the RANKING, plus a sensitivity note: which single weight change would flip the top choice. 4. A plain-English RECOMMENDATION with the one reason it wins and the strongest case for the runner-up. Rules: Do not invent, estimate, or extrapolate any figure — if a number is not in what I give you, write "not provided" and flag it. Mark every claim I should verify externally before relying on it. Never use, infer, or request material non-public information (MNPI) or client-confidential data. Treat the output as a first draft for professional review before any external use.
How to use this prompt
- Copy the full prompt above with the Copy button.
- Add your context. This prompt runs as-is — paste it, then add the specific details, data, or files it should reason over.
- Paste into ChatGPT, Claude, or Gemini and run. Read the reality guardrail below before you act on the output.
Why this prompt works
Decision matrices become decoration when weights are hidden or scores invented; forcing justified weights, an honest 'needs input' where data is missing, and a flip-sensitivity turns the exhibit into a real decision mechanism.
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When should I use this prompt?
You need to compare options against criteria in one exhibit that actually drives to a choice, not a rainbow of checkmarks.
Why does this prompt work?
Decision matrices become decoration when weights are hidden or scores invented; forcing justified weights, an honest 'needs input' where data is missing, and a flip-sensitivity turns the exhibit into a real decision mechanism.
What mistake does this prompt help you avoid?
PF09
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