AI is getting smarter-but only when it has the right information. Retrieval-Augmented Generation (RAG) is the breakthrough technique that transforms large language models into accurate, explainable, and trustworthy AI systems. Whether you're a beginner or an experienced developer, this book makes RAG easy to understand and simple to build.Designed for clarity and real-world use, RAG for Everyone walks you step-by-step through the fundamentals, tools, and best practices behind modern augmented generation systems. You'll learn exactly how to give AI access to the right knowledge, reduce hallucinations, and create applications that people can trust.What You'll Learn✔ What RAG actually is - explained in simple, intuitive terms✔ How to build a complete retrieval pipeline from scratch✔ Embeddings, chunking, and indexing made beginner friendly✔ Vector search with FAISS, Chroma, Pinecone, and other tools✔ How to connect documents, databases, and custom knowledge sources✔ Techniques for boosting accuracy and improving relevance✔ How RAG reduces hallucinations and increases explainability✔ Real-world project examples you can follow and adapt✔ How to deploy scalable, production-ready RAG applicationsWho This Book Is ForComplete beginners curious about AIDevelopers and engineers building LLM-powered toolsStudents learning modern AI techniquesData scientists optimizing model performanceEntrepreneurs and product builders creating intelligent appsAnyone who wants AI systems that are accurate, traceable, and reliableWhy This Book WorksMost RAG explanations are too abstract or too complex. This guide is different: It's simple, practical, and focused on real results. Every chapter is written to help you understand why RAG matters and how to apply it immediately.If you've been searching for a beginner-friendly introduction to augmented generation that still delivers professional-level insights, this is the book that finally makes RAG accessible to everyone.