SmartLexicon : A Retrieval Augmented Generation based Application

Author : Kalim Naushad Mulani

Abstract : — SmartLexicon has been called a state of the art Retrieval-Augmented Generation system aimed at making the AI-generated response more accurate and trust-worthy within some specialized domains like healthcare, law, and engineering. While earlier generative AI models relied heavily on static old datasets, SmartLexicon infused real-time data retrieval with intelligent response generation to ensure that it provides current and verifiable outputs. A key feature of SmartLexicon is its incorporation of domain specific repositories but adds citation and confidence scores to each answer from which it generates a response thereby improving transparency for building user trust. The modular architecture of the system eases adaptation across different sectors, thus being a f lexible solution for different use cases. This real-time, citation-backed approach essentially knocks down the credibility barrier for AI when mission-critical applications are concerned and heralds SmartLexicon as an innovative tool in the field of information retrieval as applicable in any professional domain-pivoting reliability and efficiency.

Keywords : SmartLexicon, RAG, real-time data retrieval, AI responses, domain-specific repositories, modular architecture, information retrieval, data validation, dynamic response generation.

Conference Name : International Conference on Engineering & Technology (ICET - 25)

Conference Place : Pune, India

Conference Date : 20th Dec 2025