Recommended Prerequisites:
- Basic AI Knowledge: Familiarity with fundamental AI concepts and applications to engage with advanced topics.
- Programming Skills: Knowledge of programming languages such as Python or R.
- Data Analysis Proficiency: Ability to analyze and interpret data effectively.
- Machine Learning Knowledge: Understanding of machine learning algorithms and techniques.
- Ethical Awareness: Awareness of ethical issues related to AI development.
Course Outline:
Lesson 1: Foundations of Artificial Intelligence (AI) and Prompt Engineering
- 1.1 Introduction to Artificial Intelligence
- 1.2 History of AI
- 1.3 Machine Learning Basics
- 1.4 Deep Learning and Neural Networks
- 1.5 Natural Language Processing (NLP)
- 1.6 Prompt Engineering Fundamentals
Lesson 2: Principles of Effective Prompting
- 2.1 Introduction to the Principles of Effective Prompting
- 2.2 Giving Directions
- 2.3 Formatting Responses
- 2.4 Providing Examples
- 2.5 Evaluating Response Quality
- 2.6 Dividing Labor
- 2.7 Applying The Five Principles
- 2.8 Fixing Failing Prompts
Lesson 3: Introduction to AI Tools and Models
- 3.1 Understanding AI Tools and Models
- 3.2 Deep Dive into ChatGPT
- 3.3 Exploring GPT-4
- 3.4 Revolutionizing Art with DALL-E 2
- 3.5 Introduction to Emerging Tools using GPT
- 3.6 Specialized AI Models
- 3.7 Advanced AI Models
- 3.8 Google AI Innovations
- 3.9 Comparative Analysis of AI Tools
- 3.10 Practical Application Scenarios
- 3.11 Harnessing AI’s Potential
Lesson 4: Mastering Prompt Engineering Techniques
- 4.1 Zero-Shot Prompting
- 4.2 Few-Shot Prompting
- 4.3 Chain-of-Thought Prompting
- 4.4 Ensuring Self-Consistency in AI Responses
- 4.5 Generate Knowledge Prompting
- 4.6 Prompt Chaining
- 4.7 Tree of Thoughts: Exploring Multiple Solutions
- 4.8 Retrieval Augmented Generation
- 4.9 Graph Prompting and Advanced Data Interpretation
- 4.10 Application in Practice: Real-Life Scenarios
- 4.11 Practical Exercises
Lesson 5: Mastering Image Model Techniques
- 5.1 Introduction to Image Models
- 5.2 Understanding Image Generation
- 5.3 Style Modifiers and Quality Boosters in Image Generation
- 5.4 Advanced Prompt Engineering in AI Image Generation
- 5.5 Prompt Rewriting for Image Models
- 5.6 Image Modification Techniques: Inpainting and Outpainting
- 5.7 Realistic Image Generation
- 5.8 Realistic Models and Consistent Characters
- 5.9 Practical Application of Image Model Techniques
Lesson 6: Project-Based Learning Session
- 6.1 Introduction to Project-Based Learning in AI
- 6.2 Selecting a Project Theme
- 6.3 Project Planning and Design in AI
- 6.4 AI Implementation and Prompt Engineering
- 6.5 Integrating Text and Image Models
- 6.6 Evaluation and Integration in AI Projects
- 6.7 Engaging and Effective Project Presentation
- 6.8 Guided Project Example
Lesson 7: Ethical Considerations and Future of AI
- 7.1 Introduction to AI Ethics
- 7.2 Bias and Fairness in AI Models
- 7.3 Privacy and Data Security in AI
- 7.4 The Imperative for Transparency in AI Operations
- 7.5 Sustainable AI Development: An Imperative for the Future
- 7.6 Ethical Scenario Analysis in AI: Navigating the Complex Landscape
- 7.7 Navigating the Complex Landscape of AI Regulations and Governance
- 7.8 Navigating the Regulatory Landscape: A Guide for AI Practitioners
- 7.9 Ethical Frameworks and Guidelines in AI Development
Optional Lesson: AI Agents for Prompt Engineering
- What Are AI Agents
- Applications and Trends of AI Agents for Prompt Engineers
- How Does an AI Agent Work
- Core Characteristics of AI Agents
- Importance of AI Agents
- Types of AI Agents
