In this talk, we dive deep into the development of a state-of-the-art AI-powered chatbot tutor using Retrieval-Augmented Generation (RAG) combined with fine-tuning and sophisticated prompt engineering techniques. We will explore a step-by-step guide starting from data collection and preparation, choosing and configuring models, to fine-tuning with domain-specific datasets, and iterative evaluation for performance optimization. Additionally, the session will cover the intricacies of prompt engineering, including dynamic adjustment, contextual prompt development, and bias mitigation to enhance the chatbot’s ability to deliver precise, engaging, and relevant educational interactions. Attendees will gain insights into building scalable and effective AI-driven tutoring solutions, focusing on leveraging RAG for contextual responses and continuous prompt optimization strategies.
