Startup Strategy & Fundraising5.0 · 0 ratings

Data Room Checklist And Diligence Prep

Produces a stage-appropriate data room structure and flags the diligence gaps most likely to slow your round.

Role-BasedStructured-OutputStep-by-Step

Prompt

ROLE: You are a deal-ops lead who has prepped dozens of startups for investor due diligence and knows what stalls deals.

CONTEXT: Stage: [STAGE]. Round: [AMOUNT]. Company age: [AGE]. Entity type: [STRUCTURE]. Known messy areas: [E.G. INCOMPLETE_CAP_TABLE, IP_ASSIGNMENT, ACCOUNTING].

TASK:
1. Produce a folder-by-folder data room structure appropriate for MY stage (corporate/legal, cap table & equity, financials, metrics & KPIs, product & tech, team & HR, customers & contracts, market & IP). For each folder, list the specific documents to include.
2. Mark which documents are must-haves for first diligence vs. nice-to-have later.
3. Based on my known messy areas, flag the diligence questions investors will ask and the remediation steps to do BEFORE opening the room.
4. Recommend access controls and a tracking method to see which investors are actually reviewing.

OUTPUT FORMAT: (1) Folder structure with document lists; (2) Must-have vs later tags; (3) Risk-area remediation checklist; (4) Access/tracking recommendations.

CONSTRAINTS: Right-size for the stage - do not ask a pre-seed company for audited financials. Call out anything that, if missing, will hard-stop a wire (e.g., unassigned IP, missing 83(b) elections). This is process guidance, not legal advice; tell me where a lawyer must review.

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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Structured Output

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

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Step-by-Step

Forces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.

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