Building Question Answering Systems with LLM and Knowledge Graph for Enterprise Search

Enterprise search fails the moment an LLM hallucinates a policy date or invents a reporting relationship. Dense vector retrieval helps surface relevant documents, but embeddings alone discard the explicit relationships that define how departments, products, and compliance rules actually connect. Pairing a large language model with a knowledge graph fixes this by grounding answers in a queryable structure of entities and edges. The challenge shifts from model selection to architecture. You need reliable entity extraction, efficient graph traversal, and an inference