Natural Language Query Engine for Relational Databases using Generative AI
This work addresses the need for intuitive data access for non-technical users, though it is incremental as it builds on existing natural language processing and SQL generation techniques.
The paper tackled the problem of non-technical users struggling to query relational databases due to SQL knowledge barriers by developing a Generative AI system that translates natural language queries into SQL and generates natural language responses, enabling direct and efficient data access.
The growing reliance on data-driven decision-making highlights the need for more intuitive ways to access and analyze information stored in relational databases. However, the requirement of SQL knowledge has long been a significant barrier for non-technical users. This article introduces an innovative solution that leverages Generative AI to bridge this gap, enabling users to query databases using natural language. Our approach automatically translates natural language queries into SQL, ensuring both syntactic and semantic correctness, while also generating clear, natural language responses from the retrieved data. By streamlining the interaction between users and databases, this method empowers individuals without technical expertise to engage with data directly and efficiently, democratizing access to valuable insights and enhancing productivity.