
Policy by Prompt: Using LLMOps to Draft, Test, and Enforce Smarter Regulations
Enmanuel Bueno Abstract The emergence of large language models has revolutionized how institutions create, evaluate, and enforce public policy. Traditional regulatory development is a slow, labor-intensive process that requires the collaboration of regulators, consultants and stakeholders. It is constrained by limited modeling capacity, fragmented stakeholder feedback, and static compliance mechanisms. This paper proposes the idea of “Policy by Prompt”, a

























