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What is GraphRAG? The Hybrid Approach Explained
GraphRAG combines Knowledge Graphs with RAG for 3.4x higher accuracy. How the approach works.
12 min read2 viewsUpdated: 12/2/2025
TL;DR
GraphRAG combines Knowledge Graphs with RAG. Instead of just finding similar texts, it traverses relationships in the graph. Result: 3.4x more accurate, 100x fewer tokens, multi-hop reasoning possible.
Key Takeaways
Hybrid-Ansatz
Kombiniert Graph-Traversierung mit semantischer Suche
3,4x genauer
Deutlich höhere Präzision als Vector RAG allein
100x Kompression
Nur relevanter Subgraph wird ans LLM gegeben
Multi-Hop Reasoning
Kann Beziehungen über mehrere Ebenen verfolgen
The Problem with Classic RAG
Vector RAG finds similar text chunks but loses relationships. GraphRAG solves this by combining graph traversal with semantic search.
How GraphRAG Works
- Entity Extraction: Build a knowledge graph from your data
- Graph Traversal: Follow relationships when answering questions
- Context Compression: Extract only the relevant subgraph (100x fewer tokens)
- LLM Generation: Answer based on complete but compressed context
The Numbers
- 3.4x higher accuracy on relationship questions
- 6-90% fewer hallucinations
- 100x less token usage
- 30-150ms for 3-hop queries (impossible with Vector RAG)
Use Cases
- Business Intelligence
- CRM Integration
- Process Diagnosis
- Multi-Source Analytics
Prerequisites
- Grundverständnis von RAG
- Basis Knowledge Graphs
Effort
MVP-Scope, iterativ erweiterbar