What is naive RAG?

Naive RAG is the simplest form of Retrieval-Augmented Generation, where the system retrieves a small set of relevant document chunks for a user query and passes them directly to a language model to generate an answer.

It is often used as the starting point for building a RAG system.

How naive RAG works

A typical naive RAG pipeline looks like this:

Documents
   ↓
Chunk
   ↓
Create embeddings
   ↓
Store in vector index

User query
   ↓
Create query embedding
   ↓
Retrieve top matching chunks
   ↓
Add chunks to prompt
   ↓
Language model
   ↓
Answer

The retrieval step is usually straightforward: the system searches for chunks that are most similar to the user's query and sends the top results to the model.

Why is it called naive RAG?

The term naive RAG is commonly used to describe a basic RAG implementation with little additional processing before or after retrieval.

For example, it may not include:

• Query rewriting or expansion
• Advanced metadata filtering
• Hybrid keyword and vector search
• Reranking of retrieved results
• Context compression or deduplication
• Multi-step retrieval

Main limitations

Naive RAG can work well for simple knowledge bases, but its quality depends heavily on the initial retrieval.

Common problems include:

• Retrieving irrelevant chunks
• Missing important information
• Returning duplicated or redundant context
• Poor chunk boundaries separating related information
• Passing too much weak context to the model

These limitations are why more sophisticated approaches introduce additional processing around retrieval.

The next stage of this progression is covered in “What is advanced RAG?”.

In short, naive RAG is a basic retrieve-then-generate pipeline that searches indexed document chunks and sends the top matches directly to the language model with minimal additional optimization.