This module lays the foundation for the course by defining AI, machine learning, deep learning, and generative AI, tracing the history of AI including its key developments and failures, and exploring the spectrum from narrow to general to super AI and its relationship with generative AI and ML.

This module covers the core principles of generative AI, including its types, how machines learn, types of machine learning, data science fundamentals, and the role of prompt engineering, foundation models, and transformers.

This module provides a comprehensive understanding of large language models, covering their evolution, fundamental principles, pre-training and fine-tuning, types, significance, and real-world applications.

This module introduces learners to the key generative AI tools and technologies available, including productivity and writing tools, ChatGPT, image and video generation, and the Llama model.

This module explores the broad applications of generative AI across industries, examining its domains, capabilities, use cases, and the scope of both Gen AI and its associated tools.

This module examines specific real-world use cases of generative AI across creative, financial, scientific, and technical domains, illustrating its transformative impact in diverse industries.

This module addresses the key challenges and limitations facing generative AI today, illustrated through a Google case study, and looks ahead at future developments in the field.

This module equips learners with a practical framework for implementing generative AI within organisations, covering implementation approaches, project lifecycle management, and identifying suitable use cases across industries.

This module introduces the concepts of prompts, prompting, and prompt engineering, covering their importance, industry applications, and the foundational pillars that guide effective prompt design.

This module revisits large language models with a focus on their practical significance, applications, model parameters, and the phenomenon of hallucination in LLM outputs.

This module covers the principles and best practices of prompt design, providing structured guidance across multiple sessions on how to craft effective text prompts for generative AI systems.

This module explores advanced prompting patterns and techniques used to optimise interactions with generative AI, including persona, cognitive verifier, question refinement, flipped, template patterns, and chain-of-thought approaches.

This module addresses the ethical dimensions of prompt engineering, focusing on identifying and mitigating bias and applying ethical principles in prompt design.

This module surveys the latest research and advancements in prompt engineering, keeping learners current with the rapidly evolving frontier of AI interaction design.

This module introduces DeepSeek as an emerging generative AI tool, covering its key features, unique attributes, comparison with ChatGPT, and the future trajectory of generative AI through DeepSeek's development roadmap.