ThinkWithPrompt by SynotticThinkWithPrompt

Review a gift, hospitality, or sponsorship request for bribery risk

Checks a proposed gift, entertainment, or sponsorship against anti-bribery policy and red flags, and drafts the approval decision.

Legal & ComplianceRTFStandardLegalAnalyzeFill-in Template

The prompt

Help me review a gift, hospitality, or sponsorship request for anti-bribery risk. What is proposed: [gift / meal / event / travel / sponsorship / donation - with description] Value: [AMOUNT per person and total] Giver and recipient: [who in our company, who on the other side - role and organization] Is the recipient a government official or state-owned entity employee? [YES / NO / UNSURE] Business context: [any pending tender, contract, license, renewal, inspection, or dispute] History: [other gifts or hospitality to the same person in the last 12 months] Our policy limits and approval rules: [PASTE] Evaluate: 1. Policy check - within limits? Correct approver? Pre-approval required? 2. Red flags - timing near a business decision, lavish or frequent, cash or cash equivalents, family members invited, travel with leisure elements, requests from the recipient, payments to third parties, poor documentation. 3. Public official considerations - stricter limits, local rules, and whether the recipient's organization permits acceptance. 4. Sponsorship or donation checks - beneficiary due diligence, links to officials, a genuine purpose, and a written agreement. 5. Recommendation - approve / approve with changes (e.g. reduce value, remove spouses, pay hotel directly) / decline. 6. Decision note for the requester and a register entry. When unsure whether the recipient is a public official, treat them as one. Anti-bribery laws differ by country; escalate borderline cases to legal.

Highlighted [brackets] are blanks to replace with your own details.

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What to fill in

  • AMOUNT per person and total
  • YES / NO / UNSURE
  • PASTE

How this prompt is built

This prompt follows the RTF structure (Role, Task, Format). RTF tells the model who to be, what to do and what shape the answer should take. It is the quickest structure that still gives noticeably better results than a bare request.

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