| Description
| This course focuses on deployable healthcare use cases and trains students to build smart healthcare assistants with large language models (LLMs) through reproducible, measurable experiments. Students start with task definition and prompt-engineering labs, translating healthcare data into clear model input/output specifications and running controlled comparisons for correctness, consistency, and readability. The course then covers healthcare text processing and de-identification (data cleaning, sensitive-field masking, and governance) to meet privacy and compliance needs. Core modules emphasize Retrieval-Augmented Generation (RAG), including document chunking, embedding-based indexing, retrieval tuning, and evidence-cited answers to improve traceability and reduce hallucinations. Reliability and security are addressed via compact test sets, error attribution, prompt-injection testing, and refusal-policy design. The course ends with a 6-hour capstone demo and presentation, requiring exe
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