Vince: Graph Engineering is replacing RAG at places like Microsoft, Stanford, and Anthropic. Here's the short version of why.
Vince: Standard RAG treats your knowledge base like a pile of text chunks. You embed them, you search, you stuff the top hits into a prompt. It works. But it loses the relationships between things.
Vince: Graph Engineering flips that. Entities and edges come first. Text hangs off the structure, not the other way around. So when the model reasons, it's walking real connections instead of guessing from proximity in vector space.
Vince: The interesting wrinkle from one of the replies… you don't actually need a graph database to do this. A SQL schema with a clean ontology can carry the same weight for a lot of teams.
Vince: One thing worth trying today. Pick a small domain you know well. Model it as entities and relationships in plain SQL first. Skip Neo4j, skip the infra. See if the answers get more grounded before you add complexity.
Vince: The next leap in retrieval probably won't come from bigger embeddings. It'll come from better structure underneath.