RAG vs GraphRAG Architecture Comparison
Standard RAG and GraphRAG both ground a language model in your own data, but they do it with very different architectures. This diagram places the two pipelines side by side so you can see exactly where they diverge - and why GraphRAG unlocks capabilities that flat document retrieval cannot.
Diagram
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Overview
The left column shows Standard RAG in muted gray-blue tones: a linear pipeline that embeds the query, runs a vector search over a flat document corpus, builds an augmented prompt, and calls the LLM. Red callouts mark its limits - no multi-hop reasoning, disconnected documents, and no lasting strategic asset.
The right column shows GraphRAG in full color. A query router decides whether a question is best answered by the graph, by documents, or both, then runs a dual path: a Cypher query against the knowledge graph alongside a vector search over documents. The two result sets merge into a hybrid augmented prompt before the LLM generates a response with structured citations. Green callouts highlight its advantages - multi-hop traversal, relationship reasoning, and a reusable knowledge graph (the "corporate nervous system").
A center VS separator divides the two, and the capability table at the bottom summarizes where each approach excels. Hovering a table row gently emphasizes the diagrams above.
Lesson Plan
- Spot the divergence: Have students identify the first point in the pipeline where GraphRAG differs from standard RAG (the query router and dual path).
- Map limits to advantages: For each red callout on the left, find the green callout on the right that addresses it.
- Read the table: Discuss why both systems are "Excellent" at simple Q&A but differ sharply on relationship queries.
- When to choose which: Use the bottom annotations to argue when standard RAG is sufficient and when GraphRAG is worth the added complexity.