LLM Engineering Foundations: Building RAG and AI Agents

Modern software development has shifted from simply writing code to engineering intelligent systems around large language models. Understanding how to harness these models effectively is now an essential skill for anyone looking to build next-generation applications. This course provides a structured path to understanding the mechanics of AI and how to integrate these powerful tools into functional software. You will transform your understanding of AI from a high-level concept into a practical engineering discipline. By reading through detailed explanations and studying code implementations, you will gain the skills necessary to build applications that can reason, retrieve information, and execute complex tasks. What you'll learn: - Understand the architectural foundations of large language models and the mechanics of tokenization. - Apply advanced prompt engineering patterns to improve model reliability and output quality. - Build retrieval-augmented generation (RAG) systems using vector databases for context-aware responses. - Design autonomous AI agents capable of using external tools and managing multi-step workflows. - Implement evaluation frameworks and observability to monitor and refine AI performance. - Practice secure AI development by understanding common vulnerabilities and safety protocols. The course begins with essential terminology and foundational definitions before moving into the practical logic of RAG and agentic design. You will progress through written modules that explain how to orchestrate these components into a cohesive system. This course is designed for beginners and developers who are new to AI engineering. No prior experience with machine learning or data science is required to get started. Start building your foundation in AI engineering today.