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. In this talk, we will provide an overview explaining the key Responsible AI aspects: Explainability, Bias, and Accountability. We will then outline the Gen AI usage patterns and show how the three aspects can be integrated at different stages of the LLMOps (MLOps for LLM) pipeline. We summarize the learnings in the form of Gen AI design patterns that can be readily applied to enterprise use-cases.