Retrieval-Augmented Generation (RAG): Bridging the Knowledge Gap for Smarter LLMs and AI
Large Language Models (LLMs) have taken the digital world by storm, showcasing incredible capabilities in generating human-like text, translating languages, and assisting with complex tasks. However, these models, primarily developed through supervised and self-supervised machine learning on vast, static datasets, suffer from inherent limitations: their knowledge is frozen in time, Read more






