Generative AI architecture is the backbone of models capable of creating text, images, code, and more. This blog delves into the components and design principles behind generative AI systems, including transformers, attention mechanisms, encoder-decoder structures, and diffusion models. It explains how data flows through these systems during training and inference, enabling high-quality content generation. The blog also explores scalability, multi-modal inputs, and alignment techniques. With examples from GPT, DALL·E, and other cutting-edge models, readers will gain a technical understanding of what powers generative AI and how to architect such models for enterprise and research use.