A future-ready pathway that builds foundational skills in generative AI, prompt engineering, and responsible AI use—preparing students to thrive in an AI-powered workforce.
Prompt engineering empowers learners to direct generative AI models such as ChatGPT, Google Gemini and Microsoft Copilot—where precision-designed inputs shape meaningful outcomes. With carefully crafted prompts, structured context and iterative refinement, users steer AI to generate text, imagery and insights tailored to real-world needs. This skill has become indispensable across every industry—from healthcare and finance to retail, education and manufacturing—enabling teams to amplify productivity, personalize services and accelerate innovation. Its blend of clear language, context-aware design and rapid iteration makes it the key skill for building conversational agents, automations and emerging AI-powered solutions. From research labs to startup teams and enterprise workflows, prompt engineering is the foundational capability students need to participate in today’s AI-driven economy.
These two dynamic courses empower students to master prompt-engineering techniques for generative AI while cultivating a deep understanding of AI ethics and responsible innovation—equipping them with the fluency, insight and adaptive skill-set needed for thoughtful, real-world AI practice.
This course transforms students from casual AI users into highly skilled prompt engineers by evolving beyond basic conversational chat into high-precision prompt architecture. Centering on the mastery of Prompt Engineering, the curriculum advances from Generative AI Foundations into Structured Prompting, scaling through Advanced Prompt Patterns and the orchestration of Autonomous AI agents to the mastery of Image Generation and Visual Prompting.
This journey begins by deconstructing the AI’s two-stage architecture, the fast-thinking Prediction Engine and the slow-thinking Reasoning Engine, examining how Transformers and Attention Mechanisms enable these layers to synthesize and prioritize contextual information. Building on these foundations, students then master the mechanics of Knowledge Retrieval, learning how Vector Databases and Semantic Search provide the essential contextual grounding that ensures AI outputs are accurate and data-driven.
Students then advance to designing structured XML-based prompts, utilizing a combination of high authority verbs and precision keywords to build modular instructions for predictable, high-precision AI outputs. Leveraging this structural precision, students master dozens of Prompt Patterns, scaling from foundational techniques like Persona and Few-Shot prompting to advanced cognitive frameworks such as Chain of Verification, Steerable Reasoning, and Prompt Chaining.
Next, students build Autonomous AI Agents, independent 'workers' that solve multi-step tasks, by mastering the path from single-session design and routing to state management and orchestration.
The journey culminates in Image Generation and Visual Prompting, where students apply precise control to create high-fidelity visual assets, mastering the technical parameters that drive state-of-the-art image models.
AI Ethics is the second course in our Applied AI Foundations Pathway—a rigorous, inquiry-driven course that teaches students how to evaluate the risks, responsibilities, and real-world implications of artificial intelligence. As AI becomes embedded across every industry, understanding how to use, design, and govern these systems responsibly has become a critical skill for the future workforce. This course gives students the essential ethical framework needed to navigate a world shaped by AI and to participate thoughtfully in its continued growth.
Students explore how ethical considerations influence both AI model development and AI model use, examining topics such as bias, misinformation, transparency, privacy, accountability, environmental impact, access and equity, and legal and policy gaps. Through real-world case studies and hands-on analysis activities, students learn how to identify harm, evaluate system risks, validate outputs, and design responsible AI workflows.
By the end of the course, students can assess the ethical risks of AI systems, apply strategies to reduce bias and misinformation, evaluate the societal impacts of AI, and articulate best practices for responsible AI use—fully prepared to apply ethical reasoning in all future AI and data science coursework and real-world scenarios.