95-864 AI Model Development
6 units, Carnegie Mellon University
This is a core class for AIM students and they will receive priority for enrollment. If seats remain after AIM students are registered, those on the waitlist will be invited. Large Language Models (LLMs) are reshaping how we reason, generate structured outputs, and build intelligent systems. At the core of these capabilities are transformer-based architectures and scalable pipelines for adapting, evaluating, and deploying models in production. AI Model Development is a rigorous, hands-on course that teaches students how to design, refine, and operationalize LLM-based systems across diverse real-world applications. The course opens with a focused review of ML/DL foundations and transitions rapidly into LLM fundamentals—tokenization, embeddings, attention, and decoding. Students learn to engineer prompts, construct retrieval-augmented generation (RAG) pipelines, adapt models using fine-tuning and parameter-efficient methods (LoRA, QLoRA), and evaluate safety, alignment, and performance with tools like MMLU, GSM8K, and pass@k. In later modules, students develop agentic workflows by integrating tools, memory, and reasoning into LLM systems. Domain-specific applications are explored in software engineering and biosciences, including protein design with AlphaFold and AlphaEvolve. As one student noted, "NLX/LLM is quite useful as its contents are closely related to the latest trends of AI and the job requirements of most AI/ML engineer positions."
Prerequisites: 95-828 (min C) or 11-685 (min C) or 90-803 (min C) or 11-785 (min C) or 10-601 (min C) or 17-644 (min C)
Offered: Spring 2027, Fall 2026, Spring 2026
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