How to build robust AI applications using RAG

When integrating generative artificial intelligence into a website, current models can sometimes be inefficient in certain respects. Responses may lack sufficient clarity, contain errors, or fail to include up-to-date information about your business. 

In recent years, this challenge has been addressed through various techniques, the most significant being the use of RAG (Retrieval-Augmented Generation). RAG allows models to be supplied with the specific information a user needs at the exact moment they request it—drawing from data unique to the business implementing the system. This enables the creation of chatbots, translation tools, and information-query systems that provide personalized responses to users. Furthermore, these systems offer developers a mechanism for continuous feedback, which can be used to refine and improve the RAG implementation. 

In this session, you will learn how this technology works and the types of applications that can be built with it. We will explore several examples—including one in Drupal—that showcase all the components of such a system.