Standard RAG has a dirty secret: it treats your documents as isolated chunks of text.

No relationships. No structure. Just bags of words in a vector space.

That works fine until someone asks: "Which team members worked on both Project Alpha and the Q3 budget revision?" Now your RAG system needs to understand relationships, not just similarity.

This is where Knowledge Graphs + LLMs gets interesting.

Instead of flat document chunks, you build a graph of interconnected entities and relationships. People connected to projects. Projects connected to budgets. Budgets connected to time periods. Then you retrieve not just similar text, but connected knowledge.

Microsoft's GraphRAG proved this at scale. LlamaIndex has solid knowledge graph RAG support. Neo4j is the go-to graph database.

The practical difference I've seen: regular RAG gets you 70-80% answer quality on complex, multi-hop questions. GraphRAG gets you closer to 90%.

The implementation is harder, no question. But for enterprise use cases where accuracy on complex questions matters? Knowledge graphs are the next evolution of RAG.

And very few engineers know how to build this properly. Opportunity is sitting right there.