Staff AI Engineering Foundations: RAG, Agents, and Prompt Design

As AI systems transition from simple chat interfaces to complex enterprise applications, developers need a structured approach to design, scale, and maintain them. Understanding how to orchestrate these systems is the key to unlocking true engineering impact. This text-based course guides you through the foundational concepts of modern AI engineering, helping you move beyond basic API calls to design robust, predictable AI systems using structured patterns, advanced prompting strategies, and multi-agent workflows. What you'll learn: - Understand foundational AI engineering concepts, including model capabilities, tokens, and context windows. - Design robust prompt contracts to ensure predictable and structured outputs from language models. - Build efficient Retrieval-Augmented Generation (RAG) pipelines using vector databases and semantic search. - Configure multi-agent orchestration frameworks to automate complex, multi-step workflows. - Apply modern observability and evaluation techniques to monitor and improve AI system performance. We begin with core terminology and basic concepts of language models before progressing to architectural patterns like RAG, vector storage, and agentic workflows. Through clear written explanations and practical code snippets, you will learn to structure AI applications systematically. This course is designed for software developers and tech enthusiasts who want to transition into AI engineering, with no prior machine learning background required. Start reading today to build the skills needed to design next-generation AI systems.