GTx: Foundations of Generative AI course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
This course offers a structured, beginner-friendly introduction to Generative AI, designed to be completed in approximately 3–6 weeks with a total time commitment of 15–20 hours. Learners will gain a clear understanding of how generative models work, their real-world applications, and the ethical considerations involved in deploying AI systems responsibly. The curriculum balances conceptual learning with practical insights, making it ideal for non-technical professionals and aspiring AI practitioners.
Module 1: Foundations of Generative AI
Estimated time: 5 hours
- Introduction to generative AI and how it differs from traditional AI
- Overview of deep learning and neural networks
- How generative models create text, images, and audio
- Understanding model training processes at a high level
Module 2: Large Language Models and Generative Systems
Estimated time: 5 hours
- Architecture and function of large language models (LLMs)
- Introduction to prompt engineering and model interaction
- Techniques for text and multimodal content generation
- Strengths and limitations of current generative systems
Module 3: Multimodal and Diffusion Models
Estimated time: 4 hours
- Basics of multimodal generation (text, image, audio)
- Introduction to diffusion models in image generation
- Applications of generative AI in creative fields
Module 4: Responsible AI and Ethical Considerations
Estimated time: 4 hours
- Understanding bias, fairness, and transparency in AI
- Risks of hallucinations and misinformation in generative models
- Principles of responsible AI deployment
Module 5: Real-World Applications of Generative AI
Estimated time: 4 hours
- Use cases in business, education, and software development
- Enterprise adoption strategies for GenAI tools
- Applying generative AI in practical scenarios
Module 6: Final Project
Estimated time: 3 hours
- Select a real-world problem suitable for generative AI
- Design a conceptual solution using GenAI techniques
- Present ethical considerations and implementation strategy
Prerequisites
- Familiarity with basic computing concepts
- No coding or advanced math required
- Interest in AI applications across industries
What You'll Be Able to Do After
- Explain how generative AI systems work at a foundational level
- Describe the role of large language models and diffusion models
- Apply prompt engineering techniques to interact with AI models
- Evaluate ethical risks and responsible use of AI
- Identify practical applications of GenAI in various industries