AI+ Prompt Engineer (AIPE)

• Data scientists
• Machine learning engineers
• Software developers
• Content creators
• Individuals interested in exploring the applications of AI

Course Objectives:

• Master Prompt Engineering: Develop the skills to write effective prompts that elicit desired responses from AI models, optimizing prompt structure, language, and parameters for specific tasks and datasets.
• Understand AI Architectures: Gain knowledge of AI tools, models, algorithms, and frameworks used in prompt engineering, enabling informed selection and implementation of AI solutions.
• Master Image Model Techniques: Learn to preprocess image data, fine-tune pre-trained image models, interpret model predictions, and optimize model performance for image-related tasks.
• Acquire Project-Based Learning Skills: Apply prompt engineering and AI concepts to real-world problems through collaborative project work, enhancing problem-solving, communication, and teamwork abilities.

Module 1: Foundation 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

Module 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

Module 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

Module 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

Module 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

Module 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

Module 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

• Basic understanding of AI concepts
• Willingness to learn and apply AI tools and techniques