FE 524: Business Applications in AI Systems Development

Course Description

Catalog Description

This course explores the applied use of large language models (LLMs). Participants will explore how to use LLMs with a programming language, structures to improve LLM outputs, common AI/LLM system architectures, how to use open LLMs, and how to evaluate the reliability and accuracy of LLMs. The course includes hands-on exercises, discussions, and real-world business applications.

Prerequisites

There are no course prerequisites, but students will need (1) general comfort using a computer with a command-line application like Terminal or PowerShell and (2) knowledge of Python or a commitment to learning it.

Course Outcomes

This 1-credit course aims for students to gain an understanding of the applied use of large language models (LLMs), particularly:

how to use LLMs with a programming language
common LLM system architectures
how to use open LLMs
other related topics (harnesses, inference engines, frameworks, etc)

Concepts will be motivated by examples from quantitative finance or another business domain.

Instructors

Professor	Email	Office
Ed Loeser	eloeser@stevens.edu	Babbio 109

Course Offerings

Campus	Fall	Spring	Summer
On Campus	X	X
Web Campus

Course Resources

Textbook

There is no required textbook. All material will be introduced during class.

	Topic	Assignment
Week 1	course overview, Python setup	HW 1
Week 2	few-shot learning, Python cont’d	HW 2
Week 3	few-shot learning, REST APIs	HW 3
Week 4	LLM APIs, data extraction	HW 4
Week 5	open models, inference engines	HW 5
Week 6	tool use, agents	HW 6
Week 7	retrieval-augmented generation (RAG), embeddings, similarity search	HW 7
Week 8	RAG applications, agents, project intro	HW 8, project
Week 9	agent harnesses	HW 9
Week 10	agent traces and memory	HW 10
Week 11	agent frameworks, Model Context Protocol (MCP)	HW 11
Week 12	agent frameworks, frontends	HW 12
Week 13	coding CLIs	HW 13
Week 14	training/fine-tuning overview
Finals	project presentations