Query: The RAG query process starts with an initial question or prompt, which is then refined by a rewriter to make the intent clearer or to improve its format. The refined prompt is given to a retriever, which pulls relevant pieces of information from a large collection of data. A reranker prioritizes the data pieces to find the most important bits, and malaysia whatsapp number data top-ranked pieces are then processed by a large language model (LLM) to create a coherent and relevant response. The entire process ensures that the final answer is accurate and contextually relevant and directly addresses the user’s initial query.
Across the industry, RAG systems have become the preferred choice for domain-specific GenAI projects.
Human oversight can significantly improve RAG performance across four key areas: data quality, prompt and response management, algorithmic accuracy, and contextual understanding.
In terms of data quality, human experts can regularly audit and update datasets, ensuring they remain structured, well-formatted, and complete. They can add missing contextual metadata and maintain consistency as data ages. Human reviewers can also validate knowledge before it’s added to vector stores, improving the accuracy of information chunks.