Explore the rapidly evolving world of artificial intelligence, Transformer architectures, and generative AI through practical applications and hands-on learning. This course introduces foundational AI concepts, machine learning, deep learning, and Large Language Models (LLMs), emphasizing their capabilities and applications in professional environments. Students develop essential prompt engineering skills, including designing structured prompts, applying advanced prompting techniques, and optimizing AI-generated responses for accuracy, relevance, and efficiency. The curriculum explores Transformer architectures, tokenization, AI tools, and strategies for integrating generative AI into business operations. Learners examine responsible AI practices, including ethical considerations, bias, data privacy, security, and legal compliance. Additional topics address cost management, workflow automation, and emerging AI technologies. Through interactive exercises, practical scenarios, and a capstone project, students gain experience developing AI-powered solutions that enhance productivity, streamline processes, and support organizational goals. Upon completion, learners will be prepared to apply prompt engineering techniques and implement generative AI solutions in professional settings.
1. Foundations of AI, ML, and Generative Systems
2. Evolution of Machine Learning to Deep Learning
3. Development of Generative Models
4. Rise of GPT and the Transformer Revolution
5. Inside Transformer Architecture & the GPT Family
6. The Prompt Ecosystem
7. Prompt Types and When to Use Them
8. Tokens and Constraints in Prompt Design
9. Efficiency, Syntax and Structure in Prompt Engineering
10. Techniques and Strategies for Professional Prompt Engineering
11. Tools and Platforms for Prompt Engineering
12. Applied Prompt Engineering in Real Products
13. Ethics, Bias & Responsible Prompt Practices
14. Cost Management & Prompt Economics
15. Future Directions in AI, ML and Prompt Engineering
16. Legal and Regulatory Framework for AI
17. Build an Enterprise Prompt System (Capstone Project)
Upon successful completion of this course, students will be able to:
Explain Artificial Intelligence and Generative AI Fundamentals: Describe the evolution of artificial intelligence, machine learning, deep learning, and generative AI, including the foundational principles of Transformer architectures and Large Language Models (LLMs).
Design and Optimize Effective AI Prompts: Develop structured prompts using zero-shot, few-shot, chain-of-thought, and iterative refinement techniques to improve the accuracy, relevance, efficiency, and consistency of AI-generated responses.
Analyze Transformer Architectures and Language Models: Explain the functions of tokenization, embeddings, self-attention, and model training while examining how Transformer-based architectures influence generative AI performance and capabilities.
Develop Practical Generative AI Applications: Apply prompt engineering techniques to create AI-powered solutions, including chatbots, content generation systems, customer support applications, workflow automation, and Retrieval-Augmented Generation (RAG) systems.
Evaluate Ethical, Legal, and Operational Considerations: Assess AI-generated content for bias, fairness, privacy, security, and regulatory compliance while identifying strategies to optimize token usage, manage costs, and promote responsible AI deployment.
Implement and Evaluate Enterprise AI Solutions: Design, test, and refine an enterprise-level prompt engineering system that addresses business requirements, integrates appropriate AI tools, evaluates performance, and supports organizational productivity.
This course includes a Certificate of Completion.
This class is an independent-study course. Students will have all the resources needed to successfully complete the course within the online material. A student helpdesk is available for technical support during the course enrollment.