video1.0<iframe src="https://www.loom.com/embed/6b08d60d1b8a4c2786b2bb998fd7e959" frameborder="0" width="1728" height="1296" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>12961728Loomhttps://www.loom.com12961728https://cdn.loom.com/sessions/thumbnails/6b08d60d1b8a4c2786b2bb998fd7e959-3cb643cacd4487e3.gif318.546In this Loom, I explain how I turned my Week 7 Music Recommender into a full Applied AI system with retrieval, validation, and logging. The system retrieves relevant documents from a small knowledge base, generates answers from that context, then a validator assigns a confidence score and checks whether sources were found. It also logs every interaction as JSON files for debugging and evaluation. I tested in-scope questions like prompt engineering and retrieval augmented generation successfully, and out-of-scope overfitting triggers a fallback instead of a hallucinated answer. I did not ask viewers to take any action.