Grokking the Generative AI System Design

GenAI System Design is emerging as its own interview category at top tech companies, distinct from traditional ML System Design. The questions are different, the architectures are different, and the scale considerations (GPU compute, parallelism, inference optimization) require their own mental models. Having spent years researching adaptive AI systems and neural networks, and now leading the creation of learning content at Educative, I designed this course to bridge that gap between understanding generative AI conceptually and being able to architect these gen AI systems end-to-end. You'll learn the SCALED framework, which is a 6-step methodology for breaking down any GenAI System Design problem, then apply it across five real-world systems spanning text, image, speech, and video generation. Each case study walks through training architecture, deployment design, and the specific tradeoffs involved in that modality. Before diving into the case studies, the course covers the foundational concepts you'll need: neural networks, transformers, tokenization, embeddings, parallelism strategies, inference optimization, RAG, and fine-tuning. You'll also learn how to do back-of-the-envelope calculations for LLM training and deployment. A bonus: if you have a GenAI or ML System Design interview coming up, this will give you both the framework and the depth to handle whatever systems are asked to design.