Building Language AI with Large Language Models and Agents

Language AI Tasks

Language models have shifted from research curiosity to foundational infrastructure for software. Teams now need engineers who can prompt, fine-tune, compose, and deploy LLMs responsibly. This course builds the practical and theoretical fluency to design language systems that reason, retrieve, and act.

LLMs & Agents

Core concepts, models, and ideas behind modern language AI: transformer architectures, attention mechanisms, tokenization, in-context learning, chain-of-thought reasoning, retrieval-augmented generation, and agent planning frameworks.

Tools & Platforms

OpenAI API, Claude, Hugging Face Transformers, LangChain, LangGraph, CrewAI, ChromaDB, FAISS, Streamlit, and Python notebooks.

Modular Syllabus

A specific course syllabus is built for each audience: graduate or undergraduate, across engineering, digital health, or computer science.

Innovation Through Tools Mastery

As AI and mature libraries handle standard tasks, professional developers must focus on innovation. Student projects tackle new use cases by generating unique data and fine-tuning task-specific language models.

Innovation-First Learning

Guided Student Projects

Students begin their projects while learning the material and enrich them as new concepts arrive. Each team gives several in-class presentations for discussion and feedback.

Innovation-First Learning