Why basic RAG fails at multi-hop reasoning (and how GraphRAG fixes it)

Standard chunk-based RAG often fails on multi-hop questions and broad summaries because semantic similarity alone cannot capture relationships across documents.

GraphRAG extracts entities and relationships into a knowledge graph, then combines graph traversal with vector search to retrieve connected evidence.

The article demonstrates a Python and Neo4j-based workflow for building enterprise LLM systems that reason more accurately over dispersed information.