Naive RAG
The basic retrieve-then-generate RAG pattern: embed the query, fetch top-K chunks by similarity, paste them into the prompt, generate.
Last updated: April 26, 2026
Definition
Naive RAG is the simplest possible retrieval-augmented generation pipeline: take the user query, embed it, search a vector database for the top K most similar chunks, paste those chunks into the LLM prompt, ask the model to answer. Every RAG system in production started here. It works well enough for narrow domains with high-quality chunks and well-formed queries. It fails when queries are ambiguous, when relevant information is split across multiple chunks, when the user asks comparative or multi-hop questions, or when retrieval quality is low.
When To Use
Start with naive RAG for any new RAG project. Add complexity only when you observe the specific failure modes that complexity addresses.
Related Terms
Building with Naive RAG?
I've shipped this pattern in real production systems. If you want a second pair of eyes on your architecture, that's what I do.