IW3210 Context Engineering for Information Maneuver

This course focuses on enabling students to engineer the contexts of agentic systems to complete tasks, particularly for information dominance. The course introduces large language models (LLMs) and LLM-based artificial intelligence (AI) agents from a user perspective. Common prompt engineering methods, such as few shot prompting and chain-of-thought prompting, will be covered. Additionally, using retrieval augmented generation (RAG) systems, tool-enabled AI agents, and multi-modal systems will be covered. In this course, students will be expected to use approaches taught in the class for real-world information dominance use-cases.

Prerequisite

N/A

Lecture Hours

3

Lab Hours

2

Course Learning Outcomes

The goal of the course is to provide you with the knowledge and skills to:

• Understand how large language models (LLMs) and LLM-based artificial intelligence (AI) agents produce outputs for a prompt given a context and be able to critique these outputs

• Be able to apply common prompt engineering techniques

• Be able to apply understand the information flow of RAG

• Be able to build and apply tool-enabled AI agents

• Be able to execute prompt injection and jailbreaking