AI Engineering Fundamentals: Building and Evaluating AI Agents

As AI models become central to modern software, the challenge shifts from simply making API calls to building reliable, predictable, and production-ready AI systems. This course introduces you to the core principles of AI engineering, helping you transition from basic prompt writing to designing robust AI-driven features. You will learn how to systematically build, evaluate, and optimize AI agents and features. By reading through structured concepts and analyzing real-world code examples, you will gain the confidence to implement context engineering, tool use, and retrieval systems that deliver accurate results. What you'll learn: - Understand the foundational architecture of AI agents and LLM-powered applications - Design effective context engineering strategies and structured prompt templates - Implement Retrieval-Augmented Generation (RAG) to ground model responses in custom data - Build reliable evaluation harnesses to measure and track system performance - Configure AI agents with tool-calling capabilities to interact with external APIs - Establish production feedback loops to continuously refine and improve model accuracy The course starts with essential terminology and the mechanics of large language models, then progresses to hands-on architectural patterns, prompt engineering, and systematic evaluation techniques. You will explore practical code implementations and testing strategies to ensure your AI systems behave predictably. This course is designed for software developers, product builders, and tech enthusiasts who want to transition into AI engineering. No prior experience with machine learning or AI modeling is required, though a basic understanding of programming concepts is helpful. Start reading today to master the engineering discipline behind modern AI applications.