Every Type of RAG Explained: Naive, Advanced, Modular, Graph, Agentic
RAG has evolved far beyond simple chunk-and-retrieve. This post breaks down every major RAG architecture — Naive, Advanced, Modular, GraphRAG, and Agentic — explaining how each works, what it gets right, and where it falls short. Plus a look at vectorless approaches and how SILKLEARN thinks about knowledge differently.
Every Type of RAG Explained: Naive, Advanced, Modular, Graph, Agentic
Retrieval-Augmented Generation (RAG) has become the dominant pattern for grounding large language models in external knowledge. Instead of relying on what the model memorized during training, you retrieve relevant information at inference time and include it in the prompt.
The idea is simple; the implementation space is not. The field has evolved into a family of architectures that trade off cost, latency, accuracy, and reasoning depth in different ways.
This post walks through the major RAG variants in use today—and then offers a different mental model for the problem entirely.
Naive RAG
Naive RAG is the canonical baseline. The pipeline is linear and predictable:
- Split source documents into fixed-size chunks (e.g. 512 tokens)
