11-766 LLM Applications
Graduate course on large language models with a focus on applications, prompting, fine-tuning, and alignment.
Instructor: Prof. Daphne Ippolito and Prof. Fernando Diaz
Term: Spring
Location: Carnegie Mellon University
Time: Various sections
Course Overview
Course: 11-766 LLM Applications (Spring 2026, Carnegie Mellon University)
Instructors: Prof. Daphne Ippolito and Prof. Fernando Diaz
This course provides comprehensive coverage of large language models (LLMs), from foundational concepts to cutting-edge applications. Topics include:
- LLM Architectures: Transformers, attention mechanisms, and scaling laws
- Training Methods: Pre-training, instruction tuning, and RLHF
- Prompting Techniques: Zero-shot, few-shot, chain-of-thought, and advanced prompting strategies
- Fine-tuning & Alignment: Parameter-efficient methods (LoRA, QLoRA), preference learning, and safety alignment
- Applications: Code generation, reasoning tasks, multimodal systems, and agentic workflows
- Evaluation & Analysis: Benchmarking, interpretability, and failure mode analysis
Teaching Responsibilities
As a Teaching Assistant, I support students through:
- Office hours and technical mentorship
- Assignment design and grading
- Research guidance for course projects
Course Website
Visit cmu-llms.org for schedules, lecture materials, and assignments.