Introduction to Retrieval, Generative Models, and RAG

The rapid evolution of artificial intelligence has shifted the focus from simple search systems to complex models that generate human-like text. Understanding how these technologies connect is essential for anyone looking to build or work with modern AI. In this written course, you will trace the journey from traditional information retrieval to state-of-the-art generative language models. You will gain a clear conceptual understanding of how Retrieval-Augmented Generation (RAG) bridges the gap between searching existing data and generating new, context-aware content. What you'll learn: - Understand the fundamental differences between search-based retrieval and generative AI models - Explore how embeddings and vector databases store and retrieve semantic information - Learn the core architecture of Retrieval-Augmented Generation systems - Analyze chunking strategies and document processing workflows for optimal retrieval - Practice designing basic prompt templates that integrate retrieved context with generative outputs - Evaluate the performance and accuracy of generative responses to prevent hallucinations Starting with foundational definitions of search algorithms, the course guides you step-by-step through vector embeddings, generative mechanics, and the practical assembly of a RAG pipeline. This course is designed for beginners, developers, and tech enthusiasts who want to understand modern AI architectures without needing a background in advanced mathematics or machine learning. Start reading today to master the core concepts powering the next generation of intelligent search and content creation.