Rag Implementation

RAG (Retrieval-Augmented Generation) implementation refers to the process of integrating a retrieval mechanism into a generative AI model, enabling it to fetch and incorporate relevant information from external sources to improve the accuracy and relevance of its outputs. As AI applications become increasingly reliant on high-quality and contextually relevant data, RAG implementation is gaining traction in the tech community for its potential to enhance the performance of language models and other generative AI systems.