Democratizing Data: An Intelligent Querying System for Marine Corps Data

Abstract

This research presents the development and implementation of a text-to-Structured Query Language (SQL) system tailored for Marine Corps logistics, capitalizing upon the proven capabilities of Large Language Models (LLMs). By fine-tuning an open-source LLM on a curated Global Combat Support System - Marine Corps supply and maintenance dataset, we demonstrate how non-technical users can intuitively interact with Marine Corps data through natural language queries, enhancing data accessibility and operational decision-making. Our approach assumes a resource-constrained environment, demonstrating that fine-tuning and deploying the model on a single NVIDIA A100 graphics processing unit (GPU) is not only feasible, but also highlights the potential for local or edgebased artificial intelligence (AI) solutions. We further identify the critical importance of high-quality, representative datasets and propose a hybrid approach combining prompt engineering with fine-tuning to improve performance. Our findings culminate in concrete recommendations for the Marine Corps regarding data governance, AI integration, and workforce development.