Retrieval Augmented Generation (RAG), similar to the artistry of Remy’s Ratatouille, combines the brilliance of Large Language Models (LLMs) with the precision of information retrieval. Just as Remy layers flavors in his dish, RAG-fusion seamlessly blends vectorized documents, images, audio, and video to craft nuanced responses in AI-powered applications. And much like Anton Ego’s discerning palate, RAG-search ranking ensures that the most relevant insights rise to the top.

This video focuses on the latest architectural pattern called Retrieval Augmented Generation (RAG). With a beginner-friendly introduction to why RAG is essential. Then dive into practical implementation and design considerations. Finally, exploring different RAG variations, including RAG-fusion, multi-index, and search ranking, all of this with a pinch of "Ratatouille" (as the movie).