RAG vs GraphRAG: Architectural Evolution

Standard RAG: Document Retrieval

User Query
↓
Embedding Model
↓
Vector Search No multi-hop reasoning
↓
πŸ“„ Document Corpus Flat, disconnected documents No strategic asset created
↓
Retrieved Documents
↓
Augmented Prompt
↓
🧠 LLM
↓
Response
VS

GraphRAG: Graph + Document Hybrid

User Query
↓
Query Router graph vs document?
Graph Path
Cypher Query Relationship reasoning
↓
πŸ•ΈοΈ Knowledge Graph Multi-hop traversal Corporate Nervous System
↓
Structured Results
Document Path
Embedding + Vector Search
↓
πŸ“„ Document Corpus
↓
Retrieved Documents
↓
Hybrid Augmented Prompt Best of both: structure + context
↓
🧠 LLM
↓
Response with structured citations
CapabilityStandard RAGGraphRAG
Simple Q&Aβœ“ Excellentβœ“ Excellent
Multi-hop reasoningβœ— Poorβœ“ Excellent
Relationship queriesβœ— Very Poorβœ“ Excellent
Strategic assetβœ— Noneβœ“ Knowledge Graph
MaintenanceDocuments decayGraph improves with curation
For tactical queries, both work. For strategic intelligence, only GraphRAG scales.