4 Real-World Success Stories Where GraphRAG Beats Standard RAG
LLMs are powerful, but they have a glaring weakness: they often lack real-time, domain-specific context. For enterprises, that’s a dealbreaker.
You’ve likely seen LLMs hallucinate, forget previous prompts, or deliver generic answers. That’s because standard methods like cramming data into a limited context window or relying on expensive, slow-to-update fine-tuning simply don’t scale for real-world use cases. Fine-tuning on sensitive data also introduces serious security risks.
Instead of force-feeding everything into the LLM upfront, GraphRAG connects the model to a real-time knowledge graph, a structured, constantly updated source that can be queried as needed.
NASA had a people problem. Not a personnel issue, but a data one. With thousands of employees, overlapping projects, and deep institutional knowledge buried across PDFs, documents, and databases, finding out who knew what was nearly impossible.
To address this, NASA's People Analytics team built a People Graph using Memgraph, capturing structured relationships between people, projects, departments, and areas of expertise.
