Microsoft Research has introduced GraphRAG to improve upon RAG. As we know, LLMs are used in various sectors such as healthcare, finance, education and entertainment. LLMs leverage NLP, natural language generation (NCG) and computer vision (CV). The greatest challenge is to extend the power of LLMs beyond the data these have been trained on.

GraphRAG is an innovative method to improve RAG by using LLM-generated knowledge graphs. These can be used where typical RAG would not be enough to address the complex problem on private datasets.

RAG, as we know, uses vector similarity to determine search strategies. GraphRAG introduces knowledge graphs generated by LLMs. This modification improves the performance of the LLM in question-answer system.

RAG, in fact, addressed the issue of data not included in the training of the LLM. LLMs find it difficult to comprehend condensed semantic concepts and making connections between unrelated bits of data. GraphRAG is much more sophisticated. It performs better than baseline RAG, especially when the data is from multiple data sources. It provides an overview of topics and concepts by grouping the private dataset into relevant semantic clusters with the help of a structured knowledge graph. GraphRAG fills the context window with relevant content.