Speaker
Description
Today the advancement in Artificial Intelligence have brought us to a point where machines can assist in decision-making and interact with us in natural language. Yet beneath this impressive capability lies a fundamental limitation: these systems often rely on static knowledge, frozen at the time of their training.
Unlike humans, they do not naturally consult documents, verify facts, or adapt to new information in real time.
This presentation explores how modern AI systems overcome this limitation through a powerful architectural pattern known as Retrieval-Augmented Generation (RAG).
We will take a journey inside the memory mechanism of AI systems, where external knowledge is no longer optional, but essential. Instead of relying solely on what a model has learned, RAG enables systems to actively retrieve relevant information from external sources before generating a response.