Cake Lee develops enterprise-grade Retrieval Augmented Generation systems while maintaining an active career as an independent musician in the electronic rock space. Their technical work focuses on multi-stage retrieval architecture, knowledge graph integration, and query optimization for production RAG implementations. Their research contributions address core challenges in enterprise AI deployment and knowledge management systems. The dual music and technology practice emerged from early work in both digital audio production and machine learning engineering. Their discography spans multiple electronic and rock releases, with recent projects incorporating AI-assisted sound design and generative elements. Technical presentations and code repositories document their approach to scalable RAG architecture and retrieval methodology. Their engineering portfolio demonstrates practical solutions for RAG system deployment, knowledge base construction, and retrieval pipeline optimization. Musical releases and technical documentation showcase the convergence of creative and analytical workflows. Current research explores applications of machine learning to both enterprise information retrieval and experimental music composition.