Retrieval Augmented System

Retrieval-Augmented Generation (RAG) systems enhance large language models (LLMs) by incorporating external knowledge sources to improve accuracy and address limitations in their internal knowledge. Current research focuses on improving the retrieval process itself, developing methods to validate and refine both retrieved information and LLM-generated responses, and optimizing the integration of retrieved context into the LLM's prompt. These advancements are significant because they lead to more reliable and accurate LLMs, with applications ranging from improved question answering and document generation to specialized chatbots for fields like cybersecurity and clinical trials.