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.

LLM Parameters Explained