Co-Designing Algorithms with LLMs (CoDA-LLM)

Spring 2025-2026 (4 days, 12 + 12 hours)

Course Period: March 9 - 12, 2026

  • Lectures: Monday / Tuesday / Wednesday / Thursday @ 09:00-12:00 (Classroom: AB 3107)
  • Labs: Monday / Tuesday / Wednesday / Thursday @ 14:00-17:00 (Classroom: AB 3107)
Instructor: Mustafa MISIR (Office: WDR 2106), mustafa.misir [at] dukekunshan.edu.cn / mm940 [at] duke.edu


Algorithms drive innovation across domains such as computer science, medicine, and logistics, yet their development is often time-consuming and demanding. This intensive mini-term course introduces undergraduates to a new perspective: collaborating with Large Language Models (LLMs), under Generative Artificial Intelligence (GenAI), in the design and refinement of algorithms.

No prior background is required. Each day combines conceptual instruction with hands-on labs / practice sessions where students prompt LLMs to generate algorithms, evaluate their performance, and refine them iteratively. Emphasis is placed on teamwork, critical analysis and practical applications offering a structured yet exploratory opportunity to engage with the emerging frontier of human-AI collaboration.

By the end of this course, you will be able to:
  1. apply algorithmic thinking to classical computational problems
  2. collaborate with large language models in algorithm design
  3. evaluate and iterate on algorithmic solutions through testing and reflection
Follow Canvas for announcements and discussions

Pre-requisites

  • None


There is no official textbook for this course. Still, the following books can be used as references.

Reference Books

Algorithms & Computational Problem Solving: Generative AI (GenAI) / Large Language Models (LLMs): Natural Language Processing: Transformers: Prompt Engineering: AI Agents: LLM Application Development: GenAI in Software Development & Programming: GenAI in Healthcare: GenAI for Enterprises: GenAI in Product Management: GenAI in Finance: GenAI in Marketing & Advertising: GenAI in Education:

Lecture Notes / Slides


GenAI Models


Reference Courses


Other Books

Artificial Intelligence / Machine Learning - Quick / Easy Reads:
Machine Learning:
Python Programming:
Python Programming for Data Science / Analytics:
Data Visualization:

Other Materials / Resources