Implementation of a Rag Based learning recommendation system for computing students

This research focuses on the design of a learning recommendation system for students studying computing related courses. The system assists students to find learning resources that aligns with their area of interest. The system is built with Retrieval Augmented Generation (RAG) techniques to enhance recommendation precision and relevance. The system addresses the persistent cold start problem through a structured preference acquisition protocol during user onboarding, demonstrating an 80% performance improvement over baseline models. The architecture consists of a dual-processing framework: (1) a base recommendation engine that populates personalized categorical suggestions on the user interface, and (2) a natural language query processor that simultaneously activates both RAG-based contextual analysis and algorithmic keyword extraction mechanisms. The Result of this research revealed hybridized approach substantially improves recommendation accuracy, user engagement metrics, and adaptive responsiveness to evolving preferences. It also bridges the methodological gap between deterministic recommendation algorithms and probabilistic natural language understanding; this research contributes to the advancement of personalized information retrieval systems in academic and commercial contexts.