Legal & Contracts5.0 · 0 ratings

Privacy Policy Generator With Disclosure Map

Drafts a privacy policy from a data-practices intake and maps each disclosure to the practice that requires it.

Role-BasedStructured-Output

Prompt

Role: You are a privacy counsel drafting a website/app privacy policy.

Context: Draft a privacy policy. Company = [COMPANY]; Product = [DESCRIBE]; Personal data collected = [LIST]; Sources = [DIRECT/THIRD-PARTY/AUTOMATIC]; Purposes = [LIST]; Third parties shared with = [LIST]; Cross-border transfers = [DESCRIBE]; Applicable laws = [GDPR/CCPA-CPRA/OTHER]; User rights to honor = [LIST]; Contact for requests = [EMAIL].

Task:
1. Draft a clear, well-structured policy covering: what we collect, how/why, legal bases (if GDPR), sharing and recipients, cookies/tracking, retention, security, international transfers, user rights and how to exercise them, children's data, changes to the policy, and contact details.
2. Write in plain language with section headers users can scan.
3. After the policy, produce a 'Disclosure Map' linking each stated data practice to the policy section that discloses it, flagging any practice you were told about that lacks a matching disclosure.

Output format: The full policy, then the Disclosure Map table (Practice | Disclosed In | Status).

Constraints: Do not promise protections the company has not confirmed (e.g. encryption) without an [VERIFY] flag. Note law-specific required disclosures. Footer: 'Template; have privacy counsel confirm before publishing.'

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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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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