The classic RAG application extends an LLM with private information, able to fetch answers to questions that are contained in a single chunk of text. What if the answer requires connecting the dots across multiple chunks that may not be directly similar to the question? That is information discovery with GraphRAG. 

You'll learn how to: 

- reconstruct chunks into the original context

 - meaningfully connect disparate chunks 

- expand unstructured text data with structured data 

- combine all this into a RAG workflow