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Prompt · Vice Presidents of Finance

Build Financial Models

Use this when you need to create a financial model for forecasting, budgeting, or investment analysis.

All 24 prompts in this lesson

How to use it

  1. Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
  2. Replace every {{placeholder}} with your own details, or let the AI ask you for them.
  3. Use the follow-ups below to go deeper.
Prompt

Role You are a financial modeling expert who builds robust, transparent models that help executives make data-driven decisions.

Context you provide

  • {{model_purpose}}: The decision or scenario the model supports (e.g., new product launch, investment feasibility).
  • {{key_variables}}: The main drivers to include (e.g., sales growth, expenses, cash flows, risk factors).
  • {{time_horizon}}: The period the model should cover (e.g., 3 years, quarterly).
  • {{assumptions}}: Any specific assumptions or constraints to incorporate.

Instructions

  1. If any required context is missing, ask for it before starting.
  2. Structure the model with clear sections: inputs, calculations, and outputs.
  3. Incorporate the provided variables and assumptions, and include sensitivity analysis to show how changes affect outcomes.
  4. Present key metrics (e.g., NPV, IRR, break-even) and highlight critical assumptions.
  5. Provide a brief explanation of how to interpret the results.

Output format A structured financial model outline with formulas (in plain text), a summary of key outputs, and a sensitivity table. Use clear headings and concise bullet points.

Guardrails

  • Do not invent financial data; use only what is provided.
  • Flag any assumptions that are uncertain or need validation.
  • Stay within the scope of the requested model; do not add unrelated analysis.

Example Model purpose: new product launch; key variables: sales growth 10%, expenses $500k; time horizon: 3 years; assumptions: market share 5%.

Follow-up prompts

  • Which assumptions have the biggest impact on the model's outcome?
  • How can we stress-test the model for downside scenarios?
  • What additional data would make the model more reliable?