A Retrieval-augmented Generation application for Question-Answering in Nutrigenetics Domain

The domain of nutrigenetics investigates the complex relationship between genetic variations and individual dietary responses, encompassing a wide array of disciplines, including genomics, nutrition science, bioinformatics, and personalized medicine. This field is marked by its intricate data landscape, necessitating innovative approaches to effectively manage and interpret the vast volumes of information involved. Given nutrigenetic data sheer volume and complexity, traditional AI models often struggle to maintain comprehensive and up-to-date knowledge. In this paper, we propose an implementation of the Retrieval-Augmented Generation (RAG) strategy to address the question-answering task in nutrigenetic domain. This framework enhances the accuracy and relevancy of outputs produced by an advanced Large Language Model, circumventing the exhaustive model fine-tuning process. As a result, our RAG approach not only alleviates the computational demand but also fortifies against data leakage concerns, particularly critical in the sensitive area of nutrigenetics. The implementation of RAG in the nutrigenetic domain not only addresses the existing challenges but also paves the way for more advanced and efficient exploration of nutrigenetic data. Our proposed workflow could advance the understanding of nutrigenetic interactions and personalized nutrition.