Naive RAG

Naive RAG represents the earliest and simplest generation of RAG implementations. In this architecture, large documents are arbitrarily divided into fixed-size chunks, passed through an embedding model to create dense vector representations, and stored in a vector database. When a user submits a query, it is similarly embedded, and the system performs a basic k-Nearest Neighbors (k-NN) or Approximate Nearest Neighbors (ANN) search to retrieve the top-k most mathematically similar document chunks. Once retrieved, these chunks are directly concatenated into the LLM's context window alongside the original query. The LLM is then instructed to answer the user's question relying only on the provided context. While easy to implement, Naive RAG architectures lack advanced retrieval optimizations, meaning they do not perform query transformations, re-ranking of retrieved results, or dynamic context filtering. Because of this simplistic approach, Naive RAG frequently suffers from low precision and recall. It often retrieves irrelevant information (noise) that dilutes the context, or it misses crucial information spread across multiple documents because it relies purely on naive semantic similarity rather than true contextual intent or multi-hop reasoning.