The growing adoption of Gen AI, esp. LLMs, has re-ignited the discussion around AI Regulations — to ensure that AI/ML systems are responsibly trained and deployed. Unfortunately, this effort is complicated by multiple governmental organizations and regulatory bodies releasing their own guidelines and policies with little to no agreement on the definition of terms. Rather than trying to understand and regulate all types of AI, we recommend a different (and practical) approach in this talk based on AI Transparency — to transparently outline the capabilities of the AI system based on its training methodology and set realistic expectations with respect to what it can (and cannot) do. We outline LLMOps architecture patterns and show how the proposed approach can be integrated at different stages of the LLMOps pipeline capturing the model's capabilities. In addition, the AI system provider also specifies scenarios where (they believe that) the system can make mistakes, and recommends a ‘safe’ approach with guardrails for those scenarios.