Adaptive AI for Real-Time User Prompt Correction in Conversational Systems

In this research study, an adaptive AI model for real-time rapid correction is presented as a novel way to improve user interactions with conversational systems. The difficulty of comprehending and fixing user inputs particularly those with typos, mixed languages, and colloquial expressions increases when conversational AI is incorporated into more and more applications. The suggested solution makes use of an improved version of the MT5 (Multilingual Text-to-Text Transfer Transformer) model to improve user prompts in English, Tamil, and Thanglish, among other languages. Because the model can handle both text and voice inputs, it can adapt to a wide range of user preferences. The algorithm is trained to recognize typical faults and recommend precise repairs by utilizing a dataset that consists of pairs of wrong and corrected prompts. The paper describes the architecture of the AI system, the training approach, and the performance evaluation of the system using a testing dataset. Results show notable increases in timely clarity and user satisfaction, confirming the model’s usefulness in practical settings. The study advances conversational AI by offering a powerful tool for quick augmentation that enhances user experience.