Citation:

Nikunj Agarwal, 2025. "Leveraging Large Language Models for Intelligent Data Source Selection and Query Generation in Multi-Database Systems", ESP Journal of Engineering & Technology Advancements  5(1): 6-11.

Abstract:

The exponential growth of data across industries has led to the emergence of complex, multi-database systems, necessitating intelligent and efficient methods for data source selection and query generation. This research explores the transformative potential of Large Language Models (LLMs) in addressing these challenges. By leveraging their advanced natural language understanding and contextual reasoning capabilities, LLMs can dynamically select relevant data sources and generate optimized queries tailored to specific user inquiries and operational contexts. We propose a framework that integrates LLMs to streamline data retrieval and enhance decision-making processes across multiple domains. Our approach demonstrates its applicability in building efficient chatbots and intelligent systems that provide real-time, accurate, and context-aware responses. Additionally, this system ensures adherence to domain-specific rules and regulations while optimizing performance in handling diverse and distributed data environments. The findings highlight the versatility and efficiency of LLM-powered solutions in revolutionizing data-driven workflows across industries.

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Keywords:

Contextual Query Generation, Machine Learning for Databases, Multi-Database Query Optimization, Prompt Engineering, Large Language Models, Natural Language Processing.