Industrial Research World

  When  creating a RAG (Retrieval Augmented Generation) system , you infuse a Large Language Model (LLM) with fresh, current knowledge. The goal is to make the LLM's responses to queries more factual and reduce instances that might produce incorrect or "hallucinated '' information. A RAG system is a sophisticated blend of generative AI's creativity and a search engine's precision. It operates through several critical components working harmoniously to deliver accurate and relevant responses. Retrieval:  This component acts first, scouring a vast database to find information that matches the query. It uses advanced algorithms to ensure the data it fetches is relevant and current. Augmentation:  This engine weaves the found data into the query following retrieval. This enriched context allows for more informed and precise responses. Generation:  This engine crafts the response with the context now broadened by external data. It relies on a powerful language model

  The advent of Generative AI has sparked a wave of enthusiasm among businesses eager to harness its potential for creating Chatbots, companions, and copilots designed to unlock insights from vast datasets. This journey often begins with the art of prompt engineering, which presents itself in various forms, including Single-shot, Few-shot, and Chain of Thought methodologies. Initially, companies tend to deploy internal chatbots to bolster employee productivity by facilitating access to critical insights. Furthermore, customer support, traditionally seen as a cost center, has become a focal point for optimization efforts, leading to the development of  Retrieval Augmented Generation (RAG) systems  intended to provide deeper insights. However, challenges such as potential inaccuracies or "hallucinations" in responses generated by these RAG systems can significantly impact customer service representatives' decision-making, potentially resulting in customer dissatisfaction. A

  The advent of  Generative AI  has sparked a wave of enthusiasm among businesses eager to harness its potential for creating Chatbots, companions, and copilots designed to unlock insights from vast datasets. This journey often begins with the art of prompt engineering, which presents itself in various forms, including Single-shot, Few-shot, and Chain of Thought methodologies. Initially, companies tend to deploy internal chatbots to bolster employee productivity by facilitating access to critical insights. Furthermore, customer support, traditionally seen as a cost center, has become a focal point for optimization efforts, leading to the development of Retrieval Augmented Generation (RAG) systems intended to provide deeper insights. However, challenges such as potential inaccuracies or "hallucinations" in responses generated by these RAG systems can significantly impact customer service representatives' decision-making, potentially resulting in customer dissatisfaction. A