Contemporary database systems, while effective, suffer severe issues related to complexity and usability, especially among individuals who lack technical expertise but are unfamiliar with query languages like Structured Query Language (SQL). This paper presents a new database system supported by Artificial Intelligence (AI), which is intended to improve the management of data using natural language processing (NLP) - based intuitive interfaces, and automatic creation of structured queries and semi-structured data formats like yet another markup language (YAML), java script object notation (JSON), and application program interface (API) documentation. The system is intended to strengthen the potential of databases through the integration of Large Language Models (LLMs) and advanced machine learning algorithms. The integration is purposed to allow the automation of fundamental tasks such as data modeling, schema creation, query comprehension, and performance optimization. We present in this paper a system that aims to alleviate the main problems with current database technologies. It is meant to reduce the need for technical skills, manual tuning for better performance, and the potential for human error. The AI database employs generative schema inference and format selection to build its schema models and execution formats. This enables it to enhance performance continuously, for example, generative pretrained transformer 4 (GPT-4), in working with diverse types of databases such as relational, not only SQL or NoSQL, graph databases, and vector stores. In addition, reinforcement learning mechanisms are investigated to facilitate ongoing improvement and adaptation of performance. This research aims to render the technical expertise of the user unnecessary in performing elementary database operations. The article proposes overcoming the existing barriers by critical integration of advanced AI methods, i.e., LLMs and reinforcement learning, to create an auto-adapting, user-friendly database system. This integrated, AI-driven approach is new since it combines previously separate breakthroughs, employing modern machine learning methods to solve long-standing usability and performance issues in a unified manner. The proposed research follows different approaches to integrating state-of-the-art machine learning techniques, namely generative AI models, reinforcement learning for continuous system optimization, and relative performance comparisons. Analytical instruments include empirical case studies and comparative performance comparison with existing database technologies (SQL, NoSQL, NewSQL, graph databases) under various data scenarios. Additionally, approaches to detecting and mitigating AI-specific problems such as query hallucinations, schema drift, and ongoing schema evolution are explored and experimentally tested. Compared to existing solutions such as Massachusetts Institute of Technology (MIT's) GenSQL, which focuses on applying probabilistic models to statistical inference and analysis of table data, our solution is aimed at complete database management automation, with dynamic schema creation, adaptive performance optimization across heterogeneous data types and database systems, and natural language query processing. Further, our model explicitly integrates reinforcement learning mechanisms for continued self-improvement, addressing real-time schema evolution and query hallucinations issues that GenSQL does not explicitly handle.

Keywords: AI, SQL, Data Modeling, Relational database, Large Language Models, Technology, NLP.