Course Topics
This live online course introduces participants to the dynamic world of Large Language Models (LLMs) through a hands-on and practical lens.
The course begins with foundational topics—including text preprocessing, neural networks, and transformer architectures—and quickly moves into applied use cases. Participants will explore how LLMs power real-world applications, from AI-driven customer service to content generation, and beyond.
Click here to view the course schedule.
Course Format
Participants will learn in this live (synchronous) online course through 19 hours of interactive, expert-led lectures; hands-on coding tutorials; and conversations with industry contributors. Learners will gain direct experience using and modifying models via popular APIs—including Hugging Face and OpenAI—and dive into advanced techniques like Retrieval-Augmented Generation (RAG), vector databases, agentic workflows, and fine-tuning with LoRA.
In addition, pre-program materials will include approximately 4-6 hours of pre-recorded content to help you prepare and get the most out of the program. These materials will be available several weeks before the live program sessions begin and can be completed at your own pace. Pre-program content includes lecture-style modules introducing language models and transformer architectures, along with hands-on tutorials on preprocessing text data (e.g., tokenization, lemmatization, stemming) and using pre-trained models with Hugging Face. Reviewing this pre-program material is highly recommended for all participants. In addition, pre-program materials will also offer an optional refresher on basic Python, linear algebra, and introductory statistics; these materials are designed for those who wish to review foundational concepts that may be helpful during the program.
At the end of the program, there will be an optional, friendly hackathon where you’ll put your new LLM skills to the test in a realistic, hands-on challenge. You will be provided with a starter codebase and a problem statement—then it’s up to you to compete in building the most effective solution. Prizes will be awarded for “Best in Show,” “Most Innovative,” and “Most Elegant” implementations. Whether you’re looking to deepen your learning or just have fun with fellow participants, this optional hackathon is the perfect way to wrap up your program experience.
Course Outcomes
By the end of the course, participants will be equipped to craft effective prompts, apply advanced LLM techniques, and integrate models into real-world workflows—whether in research, industry, or creative domains.
They’ll also become part of a growing community of applied AI practitioners and receive a certificate of participation from the Harvard John A. Paulson School of Engineering and Applied Sciences.
Course Learning Objectives
- Develop AI-powered customer service applications
- Improve content creation workflows using LLMs
- Implement advanced techniques such as RAG and LoRA-based fine-tuning
- Integrate LLMs with external systems via APIs
- Build and deploy agentic frameworks for task automation
Course Prerequisites
This is an advanced, hands-on technical course. Participants are expected to have the following background:
- Programming Skills
- Proficiency in Python (essential for coding exercises and API usage)
- Familiarity with machine learning frameworks such as PyTorch or TensorFlow
- Core AI & ML Knowledge
- Understanding of fundamental AI/ML concepts, including:
- Supervised and unsupervised learning
- Loss functions and optimization
- Model evaluation metrics
- Understanding of fundamental AI/ML concepts, including:
- Neural Network Experience
- Prior exposure to basic feed-forward neural network architectures
- Some acquaintance with deep learning frameworks like PyTorch or TensorFlow
- API Integration Skills
- Basic experience using Python APIs to interact with external services
(e.g., querying models, handling API responses)
- Basic experience using Python APIs to interact with external services
- Optional Recommended Skills: While not required, the following will enhance your experience:
- Working knowledge of Git and GitHub for version control
- Comfort using the command line for environment setup and script execution
Who Should Participate
Professionals in AI, developers interested in practical AI applications, and researchers can benefit from taking this course. This course is designed for learners from all types of organizations who want to increase their individual effectiveness or who have functional responsibilities, including:
- Machine Learning Engineers
- Data Scientists
- Data Engineers
- AI/ML Research Scientists
- Natural Language Processing (NLP) Engineers
- AI/ML Product Designers or Managers
- Solutions Architects
- Data Analysts
- AI/ML Technical Program Managers
- Content Strategists
- Automation Engineers
- Innovation Strategists
- Chief Technology Officers
Harvard University welcomes individuals with disabilities to participate in its programs and activities. If you would like to request accommodations or have questions about the access provided, please contact ProfEd@seas.harvard.edu in advance of your participation. Requests for American Sign Language interpreters and/or CART providers should be made at least two weeks in advance, if possible. Please note that the University will make every effort to secure services, but that services are subject to availability.
For more information, contact SEAS Professional Education at ProfEd@seas.harvard.edu.