rag-pipeline-embed-index-retrieve-generate

IN premise — summaries/2026/08/24/wiki-Sentence_embedding.md

Created 2026-08-25T02:58:54+00:00

The Retrieval-Augmented Generation (RAG) pipeline (Lewis et al., 2020) consists of: chunk documents → generate embeddings per chunk → store in vector index → embed query → top-k similarity search → feed retrieved chunks as context to a generator.

Summary

The RAG pipeline works by breaking source documents into small pieces, converting them into numeric representations for fast search, and then handing the most relevant pieces to a language model to compose an answer. This matters because the quality of the final response depends entirely on whether the right chunks were retrieved, making the embedding and similarity-search steps the critical bottleneck where errors quietly propagate into whatever the generator produces.