DSC653 Bridging Search and Generation in AI with RAG

[1–0, 1 cr.]

This course introduces students to Retrieval-Augmented Generation (RAG) including vector databases, text embedding models, and retrieval-integrated generation pipelines.  Students will explore retrieval-integrated generation pipelines using industry-standard tools like LangChain and LlamaIndex.  Students will build a mini domain-specific RAG application (e.g., for healthcare, finance, or legal knowledge bases).  Familiarity with NLP is recommended but not required, with introductory resources provided.