ModelRefs / Naive RAG — AI Glossary
Naive RAG — AI Glossary
The basic retrieval-augmented generation pipeline: chunk documents, embed, store in vector DB, retrieve top-k by similarity, pass to LLM.
Overview
Naive RAG indexes documents as fixed-size chunks, encodes them with an embedding model, stores in a vector database, and at query time retrieves top-k chunks by cosine similarity to the query embedding. Simple to implement but fails on complex queries requiring multi-hop reasoning, synthesis, or precision beyond semantic similarity.
Reference details
| Topic | rag |
|---|---|
| Also known as | basic RAG, simple RAG |
| Last reviewed | 2026-06-24 |
Related terms
Commonly confused with
The baseline pipeline — chunk, embed, retrieve top-k, generate — and the thing every other RAG variant is defined against. Naive is descriptive, not pejorative: it is the correct starting point, and the reason to name it is so that adopting a more elaborate variant is a measured decision against this baseline rather than an assumption that more machinery is better.
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Frequently asked questions
What is Naive RAG?
The basic retrieval-augmented generation pipeline: chunk documents, embed, store in vector DB, retrieve top-k by similarity, pass to LLM.
Is Naive RAG the same as basic RAG?
Yes — basic RAG, simple RAG are common aliases for Naive RAG.
What concepts are related to Naive RAG?
Closely related concepts include advanced rag, modular rag, retrieval pipeline.