Book: RAG Pocket Guide Also by me: Database Playbook My project: Hermes IDE | GitHub — an IDE for developers who ship with Claude Code and other AI coding tools Me: xgabriel.com | GitHub You ship a RAG bot to a 4,000-employee company. The first demo question is "Who reports to the VP of Engineering's manager?" The retriever returns three pages from the org-chart deck and a 2024 all-hands transcript. The model answers confidently and gets the name wrong, because no single page in the deck contains the answer. The answer was a two-hop traversal that lived in nobody's chunk. The team blames the chunk size. They tune from 800 to 1200, add overlap, swap the embedding model. The number on Ragas crawls up by two points. The wrong-name failure stays. This is the wall every team running pure vector RAG hits eventually, and it is why almost every serious RAG vendor in 2026 (Microsoft, Neo4j, and the broader GraphRAG ecosystem on top) has been adding a graph layer underneath. This post is about why.…