Interview Preparation Guide Generation Leveraging GPT-4, ZSL and Hybrid Techniques

This paper presents a novel approach for automating the creation of personalized interview preparation guides using GPT-4, Zero-Shot Learning (ZSL), and hybrid techniques. Our system dynamically generates tailored interview questions and preparation materials based on specific job descriptions and candidate profiles. By integrating advanced NLP with rule-based methods, it significantly enhances the relevance and quality of the content compared to traditional methods. We analyzed data from 10,000 job descriptions, 10,000 candidate profiles, and 20,000 interview questions to ensure high-quality results. The use of ZSL enables the generation of guides even for new and unfamiliar roles. Our approach achieved a 35% increase in question relevance and a 28% improvement in candidate-job matching accuracy compared to baseline methods. This research also paves the way for enhancing AI tools in HR, supporting personalized learning, performance reviews, and career planning.