Ananth Nagaraj

This paper navigates challenges faced by foundational language models, particularly combating Hallucinations in models like GPT. Detailing a methodical strategy, it regulates GPT's responses by tapping into knowledge solely from PDF, HTML, and Word Doc sources. The approach, integral to a regulatory Query Bot application, prioritizes hallucination-free responses. Leveraging Retriever Augmented Generation, unique prompt engineering, and a scoring mechanism for context passages, it ensures accurate responses despite GPT's innate limitations. This solution significantly reduces turnaround time by 150%, introduces Query Status Tracking, and enhances User Experience, marking a pivotal advancement in controlling misinformation while harnessing the potential of large language models.