Standard RAG failed spectacularly on a project last quarter.

The question: "Which of our engineers have worked on both computer vision AND NLP projects in the last two years?"

The chunks containing engineer profiles, project descriptions, and skill mappings were all in the vector store. But the semantic search retrieved fragments that were individually relevant but couldn't piece together the cross-referential answer.

This is exactly the type of question Graph RAG was built for.

Instead of flat text chunks, we built a knowledge graph: engineers connected to projects, projects connected to skills, skills connected to domains. The query becomes a graph traversal, not a similarity search.

The implementation: extract entities and relationships from documents using an LLM. Store them in Neo4j. When a question comes in, determine if it's a simple retrieval (use standard RAG) or requires relationship reasoning (use graph traversal + LLM synthesis).

The hybrid approach — standard RAG for most questions, Graph RAG for complex relational questions — gives the best of both worlds.

Microsoft published their GraphRAG paper, and LlamaIndex has solid support. The pattern is proven. But it's still complex enough that few teams implement it well.

That complexity is your moat if you master it.