Code Pilot Rag Powered Platform

Abstract: — The raising demand for skilled software developers has highlighted the importance of personalized, context-aware, and accessible learning platforms. Traditional online resources often fail to provide individualized mentorship and real-time guidance, leaving learners with fragmented knowledge and limited career preparation support. an intelligent mentorship system that leverages Retriever-Augmented Generation (RAG) and Large Language Models (LLMs). Code Pilot is designed as a mobile-first application that assists students and job seekers throughout their coding and career journey. The system integrates curated knowledge bases with AI-driven retrieval and generation techniques to deliver accurate, contextual, and personalized responses. It provides step-by-step explanations of coding concepts, system design guidance, resume analysis, and interview preparation material, making it a comprehensive career- enablement tool. To tackle these challenges, Code Pilot is proposed as an intelligent mentorship system knowledge base construction, RAG pipeline integration, and rigorous testing before deployment on cloud infrastructure. By combining scalability, accessibility, and personalization, Code Pilot aims to democratize technical mentorship. The work adds value to the growing domain of AI-powered education by demonstrating how RAG-driven platforms can transform self-learning into an interactive, personalized, and career-focused experience. Index Terms: Artificial Intelligence (AI), Large Language Models (LLMs), Retriever-Augmented Generation (RAG), Intelligent Tutoring Systems, Coding Assistance, Career Guidance, Resume Analysis, Interview Preparation, Mobile Learning, Cloud Deployment.