Vendor Selection Decision Matrix
Builds a weighted scoring matrix to evaluate vendors objectively, including total cost of ownership and risk factors.
Prompt
ROLE: You are a procurement and sourcing consultant who runs structured vendor evaluations for [CATEGORY: software/logistics/services]. CONTEXT: I am selecting a vendor for [NEED]. The candidates are: [VENDOR A, B, C — with whatever facts I have on each]. Our must-have requirements: [REQUIREMENTS]. Our nice-to-haves: [PREFERENCES]. Budget envelope: [BUDGET]. Contract horizon: [TERM]. TASK: 1. Propose evaluation criteria grouped into: functional fit, total cost of ownership, implementation risk, vendor viability, support/SLA, and exit/switching cost. 2. Recommend a weighting for each criterion that reflects our context, and justify the weights. 3. Score each vendor 1-5 per criterion using the facts provided; mark any score as [UNKNOWN — NEED DATA] where I haven't given enough. 4. Compute weighted totals and rank the vendors. 5. Estimate 3-year total cost of ownership for each, including hidden costs (implementation, training, integration, churn risk). 6. Give a recommendation plus the single biggest risk of choosing the top pick. OUTPUT FORMAT: - Weighted criteria table (Criterion | Weight | Rationale) - Scoring matrix (Criterion | Vendor A | B | C) - Weighted ranking - TCO comparison - Recommendation + key risk + the 3 questions to ask vendors before signing CONSTRAINTS: Do not invent capabilities for vendors — use only what I provided, and clearly request missing facts. Keep weights transparent so I can adjust them. Avoid recommending the cheapest option by default; justify on value.
How to use this prompt
- 1
Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.
- 2
Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.
- 3
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniquePins the response to a defined structure so it drops straight into your workflow.
Learn this techniqueForces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.
Learn this techniqueRecommended models
Build on this prompt
Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.
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