PlaybookPrompts

Score a build-vs-buy-vs-partner decision with a weighted framework

Business Strategy decision-makingproduct-strategyoperations

When a capability gap surfaces, founders default to building. This prompt structures the decision so cost, speed, and strategic fit are weighed explicitly before any engineering sprint kicks off.

Prompt 
You are a strategic advisor helping an operator make a capability decision. The capability or feature we need is {{CAPABILITY_DESCRIPTION}}. Our company stage and constraints are {{COMPANY_STAGE_AND_CONSTRAINTS}}. The three options we are evaluating are build internally, buy/license an existing solution, or partner with {{PARTNER_OR_VENDOR_CONTEXT}}.

1. List the five criteria most relevant to this decision (e.g., time-to-market, total cost of ownership, strategic control, integration risk, team bandwidth). Adjust the list if my context makes a standard criterion irrelevant.
2. Assign a weight (must sum to 100%) to each criterion based on my stated constraints.
3. Score each of the three options (Build / Buy / Partner) on a 1–5 scale per criterion. Show your reasoning for each score in a single sentence.
4. Calculate a weighted total score for each option and state the recommended path clearly.
5. List the two or three conditions under which the second-ranked option would become the right answer instead.
6. Flag any information I would need to gather before this recommendation becomes reliable.

Do not soften the recommendation. If one option is clearly dominant, say so.
Variables to fill in
  • {{CAPABILITY_DESCRIPTION}}
  • {{COMPANY_STAGE_AND_CONSTRAINTS}}
  • {{PARTNER_OR_VENDOR_CONTEXT}}

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: {{CAPABILITY_DESCRIPTION}}{{COMPANY_STAGE_AND_CONSTRAINTS}}{{PARTNER_OR_VENDOR_CONTEXT}}.
  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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