PlaybookPrompts

Build a weighted scoring matrix to rank competing options

Data & Analysis decision-matrixprioritizationscoringstrategy

When a team debates options with gut feel, decisions stall or get made by the loudest voice. A weighted scoring matrix makes the criteria explicit and the trade-offs visible—this prompt builds one you can drop straight into a spreadsheet.

Prompt
I need to choose between several options and want a structured scoring matrix. Here is the context:

- Decision I am making: {{DECISION_DESCRIPTION}}
- Options to evaluate: {{OPTIONS_LIST}}
- Criteria that matter to me (and rough importance if I have it): {{CRITERIA_AND_WEIGHTS}}
- Any hard constraints (options that fail these are disqualified): {{HARD_CONSTRAINTS}}

Please do the following:

1. Propose a final set of 4–7 criteria. If I provided importance levels, convert them to weights that sum to 100. If I did not, suggest weights and explain the rationale briefly.
2. Build a scoring matrix table: rows = options, columns = criteria. Score each cell 1–5 and explain in one sentence why you gave that score. Add a weighted total column.
3. Rank the options by weighted total. Call out if two options are within 5 points of each other (effectively a tie).
4. Identify the single criterion where the ranking is most sensitive—i.e., if that weight increased by 20 points, which option would move up or down.
5. Note limitations: this method works best when criteria are independent. Flag any criteria I listed that seem correlated, which could inflate their combined weight.
Variables to fill in
  • {{DECISION_DESCRIPTION}}
  • {{OPTIONS_LIST}}
  • {{CRITERIA_AND_WEIGHTS}}
  • {{HARD_CONSTRAINTS}}

How to use this prompt

  1. Copy the prompt above (Copy button on the top-right).
  2. Replace each {{VAR}} with your own value. Variables: {{DECISION_DESCRIPTION}}{{OPTIONS_LIST}}{{CRITERIA_AND_WEIGHTS}}{{HARD_CONSTRAINTS}}.
  3. Paste it into one of the recommended tools below.
  4. Iterate: tighten constraints in the prompt if the output is generic.

Why this prompt is structured this way

The prompt is split into explicit steps because LLMs do better when the path is named, not implied. Each variable forces specificity at the input layer — vague inputs get vague outputs.

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