Retrieval-augmented generation (RAG) applications excel at answering simple questions by integrating data from external sources into LLMs. But they struggle with multi-part questions that require connecting the dots across several pieces of related information. That’s because RAG applications require a database designed to store data in a way that makes it easy to find all the pieces needed to answer these types of questions.
Knowledge graphs are well-suited for handling complex, multi-part ...
Read it: How to Improve Multi-Hop Reasoning with Knowledge Graphs and LLMs