Food research is uniquely intertwined with everyday life and necessitates the utilization of big data. Within this domain, the research data consist of various forms and formats, encompassing biological experiment results, chemical analysis data, nutritional information, microbiological data, sensor data, images, and videos. This diversity stems from the integration of data from various subdomains within the larger field. With recent advancements in deep learning technology, the importance of data has grown significantly, resulting in increased reliance on data-driven research. Although specialized platforms for sharing and utilizing data have been established at the national level, particularly in the bioscience field, food research lacks a dedicated infrastructure and specialized data-sharing platforms. In this study, we develop a platform that leverages Hadoop-based distributed file systems to create a data lake. This platform enables data storage and sharing through a web-based interface. The distributed file system supports scalability by adding data nodes, making it an effective solution for capacity expansion. In addition, the web-based platform ensures high accessibility, allowing users access from anywhere, at any time, using any device. Finally, we introduce the establishment of a 1.8 PB Hadoop-based physical storage system and present an approach for building a highly accessible web platform with substantial utility.

In the realm of Generative AI, where various models are introduced, prompt engineering emerges as a significant technique within natural language processing-based Generative AI. Its primary function lies in effectively enhancing the results of sentence generation by large language models (LLMs). Notably, prompt engineering has gained attention as a method capable of improving LLM performance by modifying the structure of input prompts alone. In this study, we apply prompt engineering to Korean-based LLMs, presenting an efficient approach for generating specific conversational responses with less data. We achieve this through the utilization of the query transformation module (QTM). Our proposed QTM transforms input prompt sentences into three distinct query methods, breaking them down into objectives and key points, making them more comprehensible for LLMs. For performance validation, we employ Korean versions of LLMs, specifically SKT GPT-2 and Kakaobrain KoGPT-3. We compare four different query methods, including the original unmodified query, using Google SSA to assess the naturalness and specificity of generated sentences. The results demonstrate an average improvement of 11.46% when compared to the unmodified query, underscoring the efficacy of the proposed QTM in achieving enhanced performance.