RAG Is Dead, Long Live Graph Memory
Vector search alone can't answer questions that span relationships. The next retrieval paradigm thinks in graphs.
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Why flat vectors fall short
RAG works when the answer lives in one document. It collapses on questions that require joining facts across many sources — "which vendors in our stack have had incidents this quarter?"
Graph memory layers add entity resolution and relationship traversal on top of embeddings, and early production numbers show retrieval precision jumping from ~60% to ~85% on multi-hop questions.
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