Designing ChatGPT-Style System Architecture for Scalable AI

Building a conversational AI is easy, but scaling it to handle millions of real-time queries requires a robust, well-designed system architecture. This course demystifies the infrastructure behind large language model (LLM) applications, teaching you how to design dependable, high-throughput systems.\n\nYou will transition from simply calling APIs to architecting complete, production-ready conversational AI pipelines. You will understand how data flows from the user prompt to the model and back, ensuring low latency and high availability.\n\nWhat you'll learn:\n- Understand the foundational components of ChatGPT-style system architectures, including tokenizers, model hosting, and API gateways.\n- Design scalable data pipelines using Retrieval-Augmented Generation (RAG) and vector databases for context-rich conversations.\n- Configure caching strategies and semantic caching to reduce LLM computation costs and latency.\n- Apply rate limiting, queueing, and load balancing techniques to handle traffic spikes gracefully.\n- Explore modern MLOps concepts for monitoring model performance, drift, and system health in real-time.\n\nWe begin with essential terminology and the core mechanics of LLM text generation before moving step-by-step through database selection, context management, and system scaling strategies. Through written explanations and architectural exercises, you will design a complete blueprint for a scalable AI application.\n\nThis course is designed for software engineers, system architects, and tech enthusiasts who want to understand the backend of modern AI. No advanced machine learning background is required.\n\nStart reading today to master the architectural patterns powering modern conversational AI.