What are the core components of a RAG pipeline?

A RAG pipeline has four core components: an embedding model that converts text into vectors, a vector database that stores and searches those vectors, a retriever that finds the most relevant chunks for a query, and a generator language model that produces the final answer. Most production systems add a chunking step before embedding, a reranker after retrieval to improve precision, and a prompt template that combines the query with retrieved context. The full flow is ingest, embed, store, retrieve, rerank, and generate.