What is RAG Pipeline?
TL;DR
The end-to-end processing flow in a RAG system, from data retrieval to answer generation.
RAG Pipeline: Definition & Explanation
A RAG Pipeline is the end-to-end data processing workflow within a RAG (Retrieval-Augmented Generation) system. A typical pipeline consists of document preprocessing (text extraction, chunk splitting), embedding generation, storage in a vector database (index building), user query embedding, similarity search to retrieve relevant chunks, combining retrieved information with the prompt, and LLM-based answer generation. Optimization at each stage — chunk size, embedding model selection, reranking, prompt template design — affects the quality of the final output. Frameworks like LangChain, LlamaIndex, and Dify enable efficient RAG pipeline construction. RAG pipelines are widely used for enterprise document search, customer support, and knowledge management.