Graph RAG combines knowledge graphs with retrieval to surface connected entities and relationships. Standard RAG limitation: Retrieves independent chunks, misses relationships across documents, can't answer "how does X relate to Y" well. Graph RAG approach: Build knowledge graph from documents (entities + relationships), for queries: identify relevant entities → traverse graph → retrieve connected information → generate answer with relationship context. Construction: Extract entities and relations using NER + relation extraction (LLM or specialized models), build graph database (Neo4j, NetworkX). Query processing: Parse query for entities → find in graph → expand neighborhood → retrieve relevant subgraph + associated text chunks. Advantages: Multi-hop reasoning (A→B→C connections), relationship-aware retrieval, entity disambiguation. Microsoft's GraphRAG: Hierarchical community summaries of entity clusters enable global queries. Use cases: Enterprise knowledge (people-projects-documents), research (papers-authors-topics), product catalogs (items-features-categories). Complexity: Graph construction expensive, maintenance overhead, query complexity. Powerful for relationship-heavy domains.