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 mode
Focus on providing precise information and statistics based on the requirements of Business Person is relentless.