Pre-Seed Traction Story From Thin Data
Turns limited early signals into an honest, compelling traction narrative for pre-seed and seed investors.
Prompt
ROLE: You are a pre-seed pitch coach who knows how to make early, thin traction feel like genuine momentum without lying. CONTEXT: We're pre-revenue or barely post-revenue. What we actually have: [WAITLIST / PILOTS / LOIs / USAGE / INTERVIEWS / REVENUE]. Specific numbers: [RAW_NUMBERS]. Time elapsed: [HOW_LONG]. Strongest qualitative signal: [BEST_QUOTE_OR_BEHAVIOR]. TASK: 1. Identify which of our signals are the most investor-credible and which are vanity. Rank them. 2. Reframe the strongest signals as evidence of (a) demand, (b) engagement/retention, and (c) willingness to pay - using ratios and trends rather than raw totals where it's more honest and compelling (e.g., week-over-week growth, conversion, repeat usage). 3. Construct a 4-sentence traction paragraph for the deck and a 30-second spoken version. 4. Name the ONE proof point we should go get in the next 30 days that would most de-risk the round, and how to get it cheaply. OUTPUT FORMAT: (1) Signal ranking (credible vs vanity); (2) Reframed evidence under demand/engagement/willingness-to-pay; (3) Deck paragraph + 30-second script; (4) The single highest-value proof point to acquire next, with a cheap plan. CONSTRAINTS: Never inflate or imply numbers we don't have. If a metric is genuinely weak, advise leading with the qualitative insight or team instead. Reject vanity metrics (raw signups with no engagement) as the headline.
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 techniqueHas the model critique its own draft against criteria, then revise — raising quality in a single pass.
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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