System Design with In-Context Learning and RAG

Modern software engineering increasingly requires integrating large language models into scalable, production-ready systems. To do this efficiently, developers must master the patterns of in-context learning and retrieval-augmented generation (RAG) to deliver accurate, domain-specific AI behaviors. This text-based course guides you through the fundamental principles of designing AI-driven systems. You will transition from understanding basic language model interactions to planning robust, context-aware architectures that securely retrieve and utilize external data in real time. What you'll learn: - Understand the foundational mechanics of in-context learning and prompt engineering. - Design retrieval-augmented generation (RAG) pipelines to connect external databases to language models. - Configure vector databases and semantic search mechanisms for efficient information retrieval. - Apply system design principles to manage latency, rate limits, and caching in AI architectures. - Evaluate system performance using modern observability and testing patterns. - Practice drafting architectural blueprints for scalable, secure, and cost-effective AI applications. You will start with core definitions and the mechanics of language models before progressing to data ingestion, vector indexing, and pipeline orchestration. Through written architectural walkthroughs and conceptual exercises, you will learn how to structure every component of a production-ready RAG system. This course is designed for software engineers, system architects, and technical beginners eager to build modern AI systems. No prior experience with machine learning or model training is required. Start reading today to master the architectural patterns shaping the future of software development.