I wrote a LinkedIn post on 2026‑10‑22 that breaks down three Retrieval‑Augmented Generation (RAG) architectures—Standard RAG, Graph RAG, and Agentic RAG—showing when each is appropriate and why the underlying retrieval layer matters.
Standard RAG embeds documents into vectors and retrieves the most similar chunks via similarity search.
It breaks down when a query needs to connect facts spread across multiple documents.
Graph RAG adds a knowledge‑graph layer on top.
During indexing, an LLM extracts entities and relationships from the documents.
During retrieval, the system traverses these connections instead of relying on embedding similarity alone.
Agentic RAG takes a different approach entirely.
Instead of a fixed retrieval pipeline, an LLM agent decides at query time which tools to invoke, which sources to query, and in what order.
- Single‑hop factual lookups → standard RAG
- Multi‑hop relationship queries → Graph RAG
- Dynamic multi‑source tasks with tool use → Agentic RAG