Master Class: Gen AI and Prompt Engineering
Target Audience:
This course is intended for individuals with a basic understanding of machine learning concepts who are interested in exploring the world of Generative AI. Students with programming experience will benefit most from the hands-on projects, but it's not mandatory.
Course Duration:
The duration can be adjusted based on the desired depth and coverage, ranging from 8 Hours to 10 Hours.
Course Goals:
Gain a comprehensive understanding of Generative AI principles, model types, and applications.
Develop practical skills in implementing Generative AI models for different tasks (e.g., text generation, image synthesis, music composition).
Navigate the ethical considerations and potential biases in Generative AI development and deployment.
Explore the future of Generative AI and its impact on various industries and creative fields.
Course Modules:
Module 1: Introduction to Generative AI
What is Generative AI?
History and evolution of Generative AI models
Differentiating Generative AI from traditional Machine Learning
Applications of Generative AI across various domains
Case studies and real-world examples
Module 2: Introduction to Prompt Engineering
What is prompt engineering?
The role of prompts in large language models (LLMs)
Benefits and limitations of prompt engineering
Applications of prompt engineering across various domains
Real-world case studies and examples
Module 3: Foundations of Prompt Engineering
Understanding language models and their internal workings
Different types of prompts: open-ended, closed-ended, and chain-of-thought
Principles of effective prompt design: clarity, conciseness, and context
Common pitfalls and mistakes in prompt engineering
Module 4: Crafting Compelling Prompts
Techniques for refining prompts: rephrasing, adding examples, and incorporating style guides
Utilizing different prompt patterns: question refinement, cognitive verification, audience persona
Using few-shot learning to guide the model with specific examples
Exploring template-based approaches and prompt libraries
Module 5: Advanced Prompt Engineering Techniques
Chain-of-thought prompting for complex narratives and reasoning
Reacting to generated outputs to refine the model's direction
Prompt engineering for creative tasks: writing different genres, music composition, image generation
Fine-tuning models with prompts for domain-specific applications
Module 6: Advanced Applications of Generative AI
Text generation: Techniques for realistic text creation, storytelling, and code generation
Image synthesis: Generating high-resolution images, manipulating existing images, and creative applications
Music generation: Composing music in different styles and genres
Generative AI for scientific discovery and drug design
Generative AI for game development and virtual worlds
Module 7: Ethical Considerations and Responsible AI
Understanding bias and fairness in Generative AI models
Mitigating potential harms of Generative AI technologies
Responsible AI development and deployment practices
Transparency and interpretability in Generative AI
Legal and regulatory landscape surrounding Generative AI
Module 8 The Future of Generative AI
Emerging trends and future directions in Generative AI research
Potential societal and economic impacts of Generative AI
Open challenges and opportunities in the field
Responsible innovation and guiding principles for development
Assessment:
Practical projects demonstrating implementation of Generative AI models
Presentations and discussions on ethical considerations and future applications
