The Secret Sauce of RAG: Vector Search and Embeddings
Retrieval-Augmented Generation (RAG) leverages the strengths of Large Language Models (LLMs) and external knowledge bases to deliver more informative and accurate outputs. Here's a breakdown of the key components focusing on data chunking, embeddings, vector databases, and their interaction
How to Make Your Generative AI More Factual
Large language models are powerful tools, but ensuring their accuracy is essential. Retrieval-Augmented Generation (RAG) emerges as a game-changer, bridging the gap between raw LLM potential and reliable, factual outputs. By harnessing the power of external knowledge bases, RAG empowers LLMs to deliver more informative, contextually relevant, and up-to-date responses across various industries. From personalized e-commerce experiences to enhanced medical diagnosis assistance, the applications of RAG are vast and hold immense promise for the future of Generative AI.
