Startup Strategy & Fundraising5.0 · 0 ratings

Valuation Justification And Comps Builder

Builds a defensible valuation case using comparable rounds, traction multiples, and dilution-based reasoning.

Role-BasedChain-of-ThoughtStructured-Output

Prompt

ROLE: You are a startup valuation advisor who helps founders justify a number without anchoring it to ego.

CONTEXT: Stage: [STAGE]. Raising: [AMOUNT]. Traction: [KEY_METRICS - ARR/growth/users]. Sector: [SECTOR]. Geography: [GEO]. The valuation I'm hoping for: [TARGET_VALUATION]. Comparable companies/rounds I know of: [COMPS_IF_ANY].

TASK:
1. Triangulate valuation from three angles: (a) recent comparable rounds at my stage/sector, (b) traction multiples (e.g., ARR multiple or growth-adjusted), and (c) the dilution-driven approach (raise amount / acceptable dilution implies post-money).
2. Reconcile the three into a defensible range and place my target inside or outside it with reasoning.
3. Explain what would justify the top of the range vs the bottom, so I know which proof points raise the number.
4. Provide a 3-sentence script for stating and defending the valuation in a meeting without sounding rigid.

OUTPUT FORMAT: (1) Three-angle valuation triangulation with the math; (2) Reconciled range + verdict on my target; (3) Levers that move me up the range; (4) Defense script.

CONSTRAINTS: Valuation is set by what an investor will pay and the dilution you can stomach, not by a DCF fantasy - frame it that way. Show the dilution math explicitly. If my target is unrealistic, say so and explain the risk of over-pricing the round (down-round and signaling risk later).

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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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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