This article discusses how integrating Graph Retrieval-Augmented Generation (Graph RAG) with rerankers can significantly improve the performance of large language models (LLMs). Graph RAG leverages knowledge graphs to enable multi-hop reasoning and reduce hallucinations, while rerankers refine the retrieved information to enhance precision. This combined approach leads to more accurate, contextually rich, and explainable outputs, making it particularly beneficial for complex, domain-specific applications. Read more..
GraphRAG reranker for enhanced LLM output
