Abstract: 

In this talk we will briefly introduce Large Language Models (LLMs) and Retrieval Augmented Generation (RAG), focusing on how RAG improves LLMs by adding retrieval mechanisms. This approach connects LLMs with various information sources, ensuring that the content they produce is relevant and up-to-date. Next, we'll talk about Knowledge Graphs (KGs), explaining how they organize facts as entities and relationships by following Web standards such as RDF and SPARQL. The final part of the talk will be about the combination of the two technologies. This combination provides LLMs with accurate facts from KGs, helping to make sure the information they generate is correct and reducing errors or made-up information. This talk aims to explain these technologies and their benefits, especially how their combination, which we call RAG KG, can be used to make Conversational AI systems e.g. chatbots more reliable and factually accurate