Leveraging Metadata in RAG Customization

Metadata plays a crucial role in improving the performance and versatility of Retrieval Augmented Generation (RAG) systems, which are used to extend the capabilities of large language models. By incorporating metadata at various stages of the RAG process, including preprocessing, retrieval, ranking, and LLM prompting, users can enhance their AI applications. Metadata can be used as filters, enrichers, or rankers, providing valuable context for the LLM to generate more accurate and helpful responses. Additionally, metadata can aid in performance tracking and continuous improvement of RAG systems. To maximize the benefits of metadata, it is essential to consider its collection, engineering, and usage from the start of a project.