Imagine having a conversation with the entire INSPIRE-HEP database—asking complex questions about particle physics research and receiving precise, contextual answers instantly. The Feynbot project aims to make this vision reality through an advanced Retrieval Augmented Generation system that transforms how researchers interact with scientific literature. This presentation unveils Feynbot's innovative approach that combines cutting-edge AI with the comprehensive INSPIRE-HEP collection. Natural language queries will unlock insights buried across thousands of research papers. Whether you're exploring theoretical frameworks, investigating experimental results, or tracking research trends, Feynbot will deliver accurate, detailed responses tailored to your specific needs. We'll explore the technical architecture behind this system, reveal proven strategies for eliminating AI hallucinations, and demonstrate how Feynbot bridges the gap between researchers' needs and RAG implementation. With tools like this, we will be able to reshape research workflow, opening new pathways for scientific exploration in particle physics.