Unlocking the Power of Retrieval-Augmented Generation (RAG) in AI
Retrieval-Augmented Generation (RAG) is an advanced method that elevates the precision and dependability of generative AI models through the integration of externally retrieved information. This approach addresses a crucial need in the field of natural language processing, where traditional large language models (LLMs) may lack specific or up-to-date knowledge required
