Retrieval-augmented generation (RAG) is a hybrid AI approach that combines retrieval-based methods with generative models to improve the quality and accuracy of generated content. This approach benefits tasks requiring factual accuracy and natural language generation, such as question-answering, summarization, or generating content based on specific knowledge. How RAG Works: RAG integrates two core components: Retrieval… Continue reading Retrieval-Augmented Generation (RAG)