In this presentation, we delve into the expansive world of generative AI design patterns, selecting five pivotal examples to explore in depth: Retrieval Augmented Generation (RAG), Cluster Pulse, State Based Agents, Guard Rails, and Auto-Prompting. These patterns represent a subset of the broader spectrum of generative AI techniques, each offering unique insights into how we can enhance the capabilities and safety of AI systems. RAG provides a method for enriching AI responses with external data, Cluster Pulse fosters creativity in AI outputs, State Based Agents ensure AI actions are aligned with specific objectives, Guard Rails establish boundaries for AI behavior, and Auto-Prompting facilitates more dynamic and context-aware interactions with AI models. The application of these patterns is demonstrated through the development of the Personalized K-8 Tutor, a project that showcases the synergistic potential of combining multiple generative AI design patterns. This educational tool leverages the strengths of each pattern to create a customized learning experience that adapts to the unique needs and preferences of individual students. By focusing on these five patterns, the presentation aims to provide attendees with a clear understanding of how generative AI can be harnessed to create innovative and impactful solutions, while also highlighting the vast array of other patterns waiting to be explored in the field of generative AI.