Retrieval augmented generation use cases: Transforming data into insights

This post details the foundational concepts and technologies behind Retrieval Augmented Generation (RAG), explaining how it combines language models with external knowledge resources. It outlines the key components (retrieval mechanism, language model) and underlying technologies (Transformer architectures, vector embeddings, neural network retrievers). The post also explores RAG's applications in machine translation, question answering, summarization, dialogue systems, personal assistants, search engines, and recommender systems. Finally, it touches upon challenges like bias, scalability, and ethical considerations, positioning Glean as an enterprise-ready RAG solution.