video1.0<iframe src="https://www.loom.com/embed/8448b2bfca1d499ba06327aa2becb7b1" frameborder="0" width="1658" height="1243" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>12431658Loomhttps://www.loom.com12431658https://cdn.loom.com/sessions/thumbnails/8448b2bfca1d499ba06327aa2becb7b1-f91f5e3c1d6c9bb4.gif323.82In this Loom, I take you through my final project, a CS Masters recommender built by adapting a music recommender show project. I show a few applicant profiles, like a budget-focused domestic person, and how the system returns the top five recommended programs with scores and a RAG-based snippet matched to their keywords. I explain the data inputs, including programs.csv with hard numbers like tuition, application fee, and GRE requirements, plus locally stored scraped content that tailors over time. I also cover the guardrail fallback when no keywords match, so we always return something. No specific action was requested from viewers.