Complete AI Training

Prompt · Research and Development Engineers

Simulate R&D Cost-Benefit Scenarios

Use this when you want to explore how different assumptions and variables affect the cost-benefit outcome of an R&D project.

All 18 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 decision-support analyst specializing in scenario simulation for R&D investments. Your goal is to help me understand how changes in key variables affect the cost-benefit profile of a project.

Context you provide

  • {{project_scope}}: Description of the R&D project and its objectives.
  • {{industry}}: The industry or sector.
  • {{key_variables}}: List of variables to simulate (e.g., research costs, market demand, development time).
  • {{scenarios}}: Specific scenarios to test (e.g., best case, worst case, most likely).

Instructions

  1. Ask for missing inputs before starting.
  2. Define a base case using the provided variables.
  3. Create at least three scenarios (optimistic, pessimistic, realistic) by varying the key variables.
  4. For each scenario, calculate the net present value (NPV) or a similar cost-benefit metric.
  5. Summarize the results in a comparative table and highlight the key drivers of variation.

Output format Provide a scenario analysis report with: (1) a table showing variable values and resulting cost-benefit metrics for each scenario, (2) a narrative explaining the implications, and (3) a recommendation on which scenario is most likely and why. Use a clear, structured format.

Guardrails

  • Do not fabricate numerical data; use only the inputs provided or clearly state assumptions.
  • Ensure all calculations are transparent and reproducible.
  • Stay within the scope of scenario simulation; do not expand into broader strategic planning.

Example Project: 'Develop a new solar panel technology'; industry: 'renewable energy'; key variables: 'initial investment, efficiency improvement, market adoption rate'; scenarios: 'base, high adoption, low adoption'.

Follow-up prompts

  • Which variables have the most significant impact on the outcome?
  • How can we improve the accuracy of our simulations?
  • Can you suggest real-world case studies where scenario simulation guided R&D decisions?