Interview Prep: A Self-Assessment Model

In the competitive landscape of modern job markets, interview readiness plays a critical role in determining the success of students transitioning into professional roles. However, many candidates face significant challenges in preparing effectively due to a lack of personalized guidance and relevant practice. Generic preparation resources often fail to address individual strengths, weaknesses, and job- role alignment, leading to uncertainty, low confidence, and underperformance. To address this gap, we propose an AI-powered self-assessment platform designed to assist students in preparing for job interviews through intelligent analysis of their resumes in comparison to job descriptions. Leveraging the capabilities of the LLaMA 8B large language model integrated with GROQ AI, our system rapidly computes a relevance score and generates a set of personalized interview questions tailored to each user's resume and the specified job role. Additional functionalities include chat history retention for tracking previous sessions, enabling continuous learning, and the implementation of natural language processing techniques for deeper analysis. The system also stores user interaction data securely in a MySQL database while offering a seamless experience through a Flask-based backend. This innovative approach not only helps users understand their job fit and likely interview content but also prepares them mentally and strategically by focusing on areas that need improvement. Ultimately, this platform aims to foster a more confident, well-prepared generation of job seekers by offering a smart, efficient, and tailored mock interview experience.