RAG & Data Foundations is written for readers who want to move from basic knowledge of large language models into building reliable, data-driven systems. It introduces the principles of Retrieval-Augmented Generation and explains why grounding an LLM in external knowledge sources is essential for accuracy, reliability, and scale. By walking through the structure of RAG pipelines and vector search, the book makes clear how embeddings and semantic search provide the backbone for intelligent applications that can reference real data instead of relying only on pretraining. Developers will gain a practical understanding of how to prepare and preprocess data so that it can be indexed effectively, and they will see how vector databases such as Pinecone, Weaviate, and Chroma make large-scale retrieval efficient and manageable. Each concept is tied to implementation practices that can be followed without requiring an advanced research background. The text demonstrates how LangChain RAG tutorials translate theory into working systems, and why frameworks like LangChain have become the standard entry point for connecting LLMs with structured and unstructured data. Alongside the technical principles, the book emphasizes the importance of building RAG systems for developers who want to extend model capabilities in meaningful ways. It explains how data pipelines for LLMs handle ingestion, chunking, and metadata, and it shows how those design choices directly influence retrieval accuracy and cost. The role of API wrappers and external tool integration is also highlighted, giving readers a view of how to extend agents beyond simple prompting into workflows that interact with real services. By the end of the book, readers will not only understand how Retrieval-Augmented Generation works but also how to apply it in practice. They will know how to connect a language model with external knowledge bases, optimize embeddings and semantic search, and set up systems that produce grounded, verifiable outputs. The goal is to provide both clarity and confidence, showing that RAG is not a theoretical buzzword but a practical design pattern that every AI practitioner should master. This book is the bridge between understanding generative AI and creating production-ready systems. For beginners in agentic AI, it isa book where concepts meet implementation, where raw data becomes organized knowledge, and where large language models evolve into reliable, context-aware applications.