Graph RAG (Retrieval-Augmented Generation)
Coined by Richard Ewing, Product Economist
Definition
Graph RAG (Retrieval-Augmented Generation) evolves standard vector-based semantic search by combining knowledge graphs with vector embeddings, allowing LLMs to reason over complex, deeply interconnected enterprise datasets. Standard RAG fails at global queries (e.g., "Summarize the entire procurement strategy") because it only retrieves the top 10 most semantically similar text chunks. Graph RAG builds an ontological map of relationships, enabling the model to traverse nodes and synthesize answers from disparate documents with massive accuracy improvements.
Why It Matters
If an enterprise relies on LLMs for legal discovery or complex financial auditing, standard RAG hallucination rates are unacceptably high. Graph RAG significantly lowers the Cost of Predictivity for complex reasoning loops.
How to Calculate
- 1Abstract entity extraction costs during indexing
- 2Measure the latency increase from multi-hop graph queries
- 3Audit hallucination reduction vs traditional BM25/Vector retrieval
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To cite this definition:
Ewing, R. (2026). "Graph RAG (Retrieval-Augmented Generation)." richardewing.io.
https://www.richardewing.io/articles/frameworks/graph-rag