Vectorless RAG Explained: Beyond Embeddings and Vector Databases Artificial Intelligence practitioners often assume that Retrieval Augmented Generation (RAG) automatically means chunking documents, embedding them, and storing them in a vector database. That assumption is understandable but technically incomplete. RAG fundamentally means augmenting a language model with retrieved external knowledge before generating an answer. The retrieval mechanism does not have to rely on embeddings or vector similarity. Recently, a new family of approaches often referred to as Vectorless RAG has gained attention. These systems retrieve information without relying on dense embeddings or vector databases. Instead, they rely on document structure, lexical…