Generative AI and Prompt Engineering

This 65–70 hour course is designed for college students across engineering, finance, marketing, arts and other disciplines who want to understand how modern AI works and apply it effectively. Starting from AI history and core models, the curriculum covers machine learning basics, generative AI use cases, natural language processing, ChatGPT models, and step-by-step prompt engineering—from fundamentals to advanced techniques.

Learners explore leading AI tools for text, images and productivity, examine ethics and responsible AI use, and understand AI-driven career paths. An optional engineering track introduces AI-assisted coding using tools like GitHub Copilot and ChatGPT. Assignments, activities and a final project help students build a beginner-level AI portfolio.

Understand key AI concepts, terminology and the evolution from rule-based systems to Generative AI.

Grasp various AI model types including reactive, limited memory and transformer-based generative models.

Learn machine learning basics such as supervised, unsupervised and reinforcement learning.

Identify real-world Generative AI applications for text, image, video and audio across industries.

Understand how AI processes language using tokens, embeddings and transformers.

Use ChatGPT and similar tools effectively through structured prompt styles and patterns.

Apply advanced prompt engineering frameworks to control creativity, structure and output quality.

Explore AI tools for writing, creativity, presentations and productivity.

Recognize ethical issues around AI including bias, misinformation, privacy and responsible usage.

Understand AI career paths and begin building an introductory AI portfolio.

1. Foundations of AI

2. Evolution of AI: Rule-based Systems to Generative AI

3. Models of AI & Generative Models

4. Machine Learning Basics

5. Generative AI Foundations & Use Cases

6. NLP for Everyone

7. ChatGPT Models & Improvements

8. Introduction to Prompting

9. Advanced Prompt Engineering

10. AI Tools & Platforms

11. Ethics, Risks & Responsible AI

12. AI Careers & Job Market

13. Optional: AI for Coding (Engineering Track)

Explain clearly what AI is, how it evolved and how Generative AI fits into the larger AI landscape.

Differentiate between traditional software, ML models and generative systems in practical contexts.

Design basic chatbot flows and AI-assisted study or work workflows using large language models.

Write structured prompts—direct, role-based and few-shot—and refine them for better results.

Apply prompt frameworks to generate resumes, lesson plans, case studies, and creative content.

Use AI tools for documents, slides, images and simple videos to enhance academic or professional work.

Participate in discussions on AI ethics, bias, misinformation, deepfakes and responsible AI usage.

Build a mini AI portfolio containing assignments, a tool-based project and an industry case study.

(Optional - Engineering Track): Use AI coding tools for code generation, debugging and documentation.