Standard RAG has a ceiling. If your query requires connecting information across multiple documents — "How did decision A lead to outcome B, which caused problem C?" — vector similarity search fails. GraphRAG, released by Microsoft Research in 2024, solves this by building a knowledge graph from your documents before any query runs. Why Standard RAG Fails at Multi-Hop Questions Vector search retrieves chunks that are semantically similar to the query. But similarity ≠ relationship. ❌ "What are all the indirect effects of policy X across departments?" ❌ "Which entities are connected to both A and B?" ❌ "What's the overall theme across this entire document corpus?" Enter fullscreen mode Exit fullscreen mode These require traversing relationships between entities — exactly what graphs are built for.…