agents

Retrieval-Augmented Generation (RAG) has emerged as a pivotal advancement in the AI landscape, particularly in enhancing the capabilities of generative models. By integrating information retrieval mechanisms with generation models, RAG systems aim to overcome the limitations of traditional AI, especially in terms of accuracy and relevance. However, despite its promising start, RAG-based solutions have faced significant challenges, leading to a nuanced discussion about their sustainability and future growth. This article delves into the rise of RAG-based solutions, their strengths, the challenges they face, and the reasons behind their potential decline. What is RAG? Retrieval-Augmented Generation (RAG) is an architecture that…