context graphs

Context graphs map high-dimensional relationships between entities to provide LLMs with structured, verifiable domain knowledge.

Context graphs solve the hallucination problem by grounding generative AI in a deterministic web of facts. By integrating vector embeddings with property graph schemas (like Neo4j or ArangoDB), these systems allow an LLM to traverse specific paths between nodes: identifying a 'Part Number' linked to a 'Supplier' and a 'Risk Rating' in milliseconds. This architecture moves beyond simple keyword matching to provide 360-degree situational awareness for cybersecurity, fraud detection, and supply chain logistics. It ensures every response is anchored to a traceable data point rather than a statistical guess.

https://www.graphistry.com/blog/context-graphs-for-ai