AI+ Prompting Fundamentals™

AI+ Prompt Engineer™ covers AI history, concepts, applications, and best practices for designing effective prompts, along with machine learning, deep learning, neural networks, and natural language processing.

What you’ll learn

By the end of the course, you’ll be well-equipped to write AI system prompts. This includes learning to build prompts that get AI models to respond, optimize prompt structure and language for specific tasks and datasets, and troubleshoot and adjust prompts to increase model performance and output quality. You will learn to preprocess image data, fine-tune pre-trained image models, understand model predictions, and maximize model performance for image classification, object identification, and image synthesis.

• Foundations of Artificial Intelligence (AI) and Prompt Engineering:
  – Introduction to Artificial Intelligence.
  – History of AI.
  – Machine Learning Basics.
  – Deep Learning and Neural Networks.
  – Natural Language Processing (NLP).
  – Prompt Engineering Fundamentals.
• Principles of Effective Prompting:
  – Introduction to the Principles of Effective Prompting.
  – Giving Directions.
  – Formatting Responses.
  – Providing Examples.
  – Evaluating Response Quality.
  – Dividing Labor.
  – Applying The Five Principles.
  – Fixing Failing Prompts.
• Introduction to AI Tools and Models:
  – Understanding AI Tools and Model.
  – Deep Dive into ChatGPT.
  – Exploring GPT-4.
  – Revolutionizing Art with DALL-E 2.
  – Introduction to Emerging Tools using GPT.
  – Specialized AI Models.
  – Advanced AI Models.
  – Google AI Innovations.
  – Comparative Analysis of AI Tools.
  – Practical Application Scenarios.
  – Harnessing AI’s Potential.
• Mastering Prompt Engineering Techniques:
  – Zero-Shot Prompting.
  – Few-Shot Prompting.
  – Chain-of-Thought Prompting.
  – Ensuring Self-Consistency in AI Responses.
  – Generate Knowledge Prompting.
  – Prompt Chaining.
  – Tree of Thoughts: Exploring Multiple Solutions.
  – Retrieval Augmented Generation.
  – Graph Prompting and Advanced Data Interpretation.
  – Application in Practice: Real-Life Scenarios.
  – Practical Exercises.
• Mastering Image Model Techniques:
  – Introduction to Image Models.
  – Understanding Image Generation.
  – Style Modifiers and Quality Boosters in Image Generation.
  – Advanced Prompt Engineering in AI Image Generation.
  – Prompt Rewriting for Image Models.
  – Image Modification Techniques: Inpainting and Outpainting.
  – Realistic Image Generation.
  – Realistic Models and Consistent Characters.
  – Practical Application of Image Model Techniques.
• Project-Based Learning Session:
  – Introduction to Project-Based Learning in AI.
  – Selecting a Project Theme.
  – AI Implementation and Prompt Engineering.
  – Integrating Text and Image Models.
  – Evaluation and Integration in AI Projects.
  – Engaging and Effective Project Presentation.
  – Guided Project Example.
• Ethical Considerations and Future of AI:
  – Introduction to AI Ethics.
  – Bias and Fairness in AI Models.
  – Privacy and Data Security in AI.
  – The Imperative for Transparency in AI Operations
  – Sustainable AI Development: An Imperative for the Future
  – Ethical Scenario Analysis in AI: Navigating the Complex Landscape
  – Navigating the Complex Landscape of AI Regulations and Governance.
  – Navigating the Regulatory Landscape: A Guide for AI Practitioners.
  – Ethical Frameworks and Guidelines in AI Development