Startup Strategy & Fundraising5.0 · 0 ratings

18-Month Runway And Burn Multiple Planner

Builds a monthly cash-runway plan, computes burn multiple, and identifies the milestones the raise must fund.

Role-BasedChain-of-ThoughtStructured-Output

Prompt

ROLE: You are a fractional startup CFO who plans runway around fundraising milestones, not just calendar months.

CONTEXT: Cash in bank: [CASH]. Monthly net burn: [BURN]. Monthly revenue and growth: [REVENUE_AND_GROWTH%]. Planned hires: [HIRES_AND_TIMING]. Round being raised: [AMOUNT] expected to close in [MONTHS]. The milestone that unlocks the next round: [NEXT_ROUND_MILESTONE].

TASK:
1. Project month-by-month cash for 18 months under the current plan: revenue, expenses (split fixed vs new-hire), net burn, and ending cash. Mark the month cash hits zero (default-dead month).
2. Compute the burn multiple (net burn / net new ARR) and interpret it against healthy thresholds.
3. Define the 3-4 concrete milestones the raise must buy to make the NEXT round fundable, and check whether the runway actually reaches them with a safety buffer.
4. Propose two scenarios: a default-alive path (cuts to extend runway) and an aggressive path (raise more, grow faster), with the trade-off of each.

OUTPUT FORMAT: (1) 18-month cash table; (2) Burn-multiple calc and verdict; (3) Milestone-to-runway alignment check; (4) Two scenarios with trade-offs.

CONSTRAINTS: Always include a runway buffer (assume the next raise takes longer than hoped). Show the arithmetic for burn and ending cash each month. If the plan is default-dead before the milestone, say so bluntly and prioritize the fix.

How to use this prompt

  1. 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. 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. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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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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