Instructed by AI pioneer Andrew Ng, Generative AI for Everyone offers his unique perspective on empowering you and your work with generative AI. Andrew will guide you through how generative AI works and what it can (and can’t) do. It includes hands-on exercises where you'll learn to use generative AI to help in day-to-day work and receive tips on effective prompt engineering, as well as learning how to go beyond prompting for more advanced uses of AI.
You’ll get insights into what generative AI can do, its potential, and its limitations. You’ll delve into real-world applications and learn common use cases. You’ll get hands-on time with generative AI projects to put your knowledge into action and gain insight into its impact on both business and society.
This course was created to ensure everyone can be a participant in our AI-powered future.
- Define generative AI, including large language models (LLMs), and describe how supervised learning is used to train generative AI models to create high quality text and images.
- List common use cases for generative AI including writing, reading, and chatting tasks for both web-UI and software-based implementations of LLMs.
- Identify the limitations of LLMs and use practical techniques and strategies for writing better prompts to enhance the quality and relevance of an LLM’s responses.
Week 1 Quiz 1: What is Generative AI?
Week 1 Quiz 2: Generative AI Applications
- Describe the key phases in the lifecycle of a generative AI project.
- Explore when to use more advanced techniques, like retrieval augmented generation (RAG), fine-tuning, and reinforcement learning from human feedback (RLHF), to customize or improve an LLMs performance.
- Think through the cost considerations of using a cloud-based LLM to power software applications.
Week 2 App Item 1: Prompting an LLM in code
Week 2 App Item 2: Reputation monitoring system
Week 2 Quiz 1: Software Applications
Week 2 Quiz 2: Advanced technologies: Beyond prompting
- Analyze workflows and determine new business opportunities that arise from generative AI’s potential to improve efficiency, productivity, and value generation.
- Recognize that AI primarily automates tasks within jobs, not entire roles, and evaluate tasks for generative AI potential based on two key criteria: technical feasibility and business value.
- Discuss the primary concerns that arise from the adoption of generative AI and LLMs, including job loss, the amplifying of humanity’s worst impulses, and human extinction.
- List the principles of responsible AI, including fairness, transparency, privacy, security, and ethical use, and learn strategies to ensure ethical and socially responsible AI development and deployment.