See how to build a RAG powered chatbot to answer HR questions on policies and get consistent, up to date results.
Large Language Models (#LLMs) predominantly create value for the business world through the #RAG methodology. Even though most of the public’s attention is focused on the quality of text these models generate, in this workshop, Superlinked‘s lead ML Engineer argues that developers can improve RAG performance much more efficiently through retrieval.
Many RAG solutions rely on datasets that contain both numeric and text data. In this video, you will learn how to combine embeddings from these different data modalities in a single vector to build a high-performing RAG system, through an example of a chatbot for HR policies:
Check out the VectorHub article for detailed instructions and try it for yourself in the GitHub repo.