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Mar 24, 2026
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AAIT 216 - Natural Language Processing
PREREQUISITES: AAIT 212 - Machine Learning
CREDIT HOURS MIN: 3
LECTURE HOURS MIN: 3
TOTAL CONTACT HOURS MIN: 48
This course provides an exploration of Large Language Models (LLMs) as the contemporary paradigm of Natural Language Processing (NLP). Students will develop a practical understanding of how modern Artificial Intelligence (AI) language technologies are designed, deployed, and leveraged across industry applications. The course examines transformer-based model architectures, deployment strategies, security considerations, tool use, and ethical considerations for modern LLM implementations.
MAJOR COURSE LEARNING OBJECTIVES: Upon successful completion of this course the student will be expected to:
- Identify the various tasks and industry use cases for NLP
- Articulate how LLMs are embedded into applications through both graphic interfaces and APIs
- Demonstrate how LLMs encode linguistic information to predict, classify, and generate text
- Analyze the fundamental features of transformer-based model architectures
- Evaluate the use of system prompts and prompt engineering to shape model behavior, set guardrails, and control outputs
- Illustrate how external knowledge sources such as documents, databases, and web searches extend model context through methods like Retrieval-Augmented Generation (RAG)
- Analyze the role of post-training and fine-tuning in model development
- Assess LLM security considerations such as attack vectors, data access controls, hosting and ownership policies, and non-deterministic security implications
- Outline the significance that LLMs play within the broader context of current computing trends
COURSE CONTENT: Topical areas of study include -
- NLP and LLM Fundamentals
- Industry Applications of Language Models
- Transformer Architecture
- API and Interface Integration
- Prompt Engineering
- Sentiment Analysis and Chatbots
- Retrieval-Augmented Generation (RAG)
- Fine-Tuning and Post-Training
- LLM Security and Governance
- Emerging Trends in AI Computing
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