Retrieval Augmented Generation (RAG) has emerged in recent years as a popular technique at the crossroads of Information Retrieval and Natural Language Generation. It represents a promising new approach that combines the strengths of both retrieval-based systems and generative AI models, aiming to address the limitations of each, while enhancing their overall performance on document intelligence tasks. This talk will introduce the key frameworks, methodologies and advancements in RAG, exploring its ability to empower Large Language Models with a deeper comprehension of context, by leveraging pre-existing knowledge from external corpora. We will review the theoretical foundations, practical applications, and technical challenges associated with RAG, showcasing its potential to impact various fields, such as document summarization or database management. Through this talk, attendees will gain insights into the most relevant topics related to RAG, including token embedding, vector indexing and semantic similarity search.