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Abstract: Retrieval-augmented generation (RAG) enhances large language models (LLMs) by integrating external knowledge sources to improve response accuracy and relevance. This chapter explores RAG's core components: retrieval mechanisms, external knowledge bases, and language generation models. The RAG pipeline involves data preparation and indexing, retrieval, and generation. Data preprocessing, text splitting, and embedding generation are crucial for efficient retrieval. The chapter details the token-to-passage embedding pipeline, where text is tokenized, embedded, contextually encoded, pooled, and stored for similarity search. RAG is retrieval-agnostic, accommodating sparse, dense, graph-structured, and hybrid retrieval methods. The chapter concludes by examining data preparation and indexing, which transform raw data into a queryable format, ensuring retrieval efficiency and response accuracy. This chapter examines structured and unstructured data, focusing on their roles in RAG systems. Structured data, organized in predefined formats like relational databases and spreadsheets, offers predictability and consistency, ideal for precise queries. Unstructured data, such as text documents and multimedia, provides depth and context but requires extensive preprocessing. Programmatic data, accessed via APIs, bridges these types, enabling real-time updates but introducing dependencies.