Foundations of RAG and Agentic AI Systems

As artificial intelligence evolves beyond simple chat interfaces, the demand for systems that can access real-time data and execute complex tasks independently is skyrocketing. Understanding how to build these intelligent systems is the key to unlocking the next generation of AI development. In this course, you will transition from writing basic prompts to designing sophisticated Retrieval-Augmented Generation (RAG) systems and autonomous AI agents. You will gain a solid conceptual foundation and practical knowledge to connect large language models to external databases and construct workflows that can plan, reason, and act. What you'll learn: - Understand the core architecture of RAG systems and how they solve common LLM limitations. - Configure vector databases to store, index, and retrieve semantic information efficiently. - Apply prompt engineering patterns to guide LLMs in reasoning and decision-making tasks. - Design autonomous AI agents capable of using tools and executing multi-step workflows. - Implement evaluation strategies to measure the accuracy and safety of your AI systems. The course begins with foundational definitions of semantic search, embeddings, and agentic reasoning before moving into step-by-step code implementations of RAG pipelines and multi-agent systems. You will read detailed explanations and analyze practical code snippets designed to build your skills progressively. This course is designed for aspiring AI developers, software engineers, and tech enthusiasts who want to learn modern AI patterns from scratch, with no prior AI engineering experience required. Start reading today to begin building intelligent, data-aware AI systems.