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Chunking is the process of splitting documents into smaller retrievable units before embedding and indexing them. In a RAG pipeline: Documents are split into chunks. Each chunk is converted into embeddings. The embeddings are stored in a vector database. User queries retrieve the most relevant chunks. The retrieved chunks are passed to the LLM as context. This means retrieval quality depends heavily on chunk quality.

In this guide, we’ll break down the major internal components of a RAG pipeline step by step in plain English. We’ll cover: chunking, embeddings, vector databases, similarity search, retrieval, and context injection into LLMs.