AI+ Prompting Fundamentals™

About Course

Course Overview

Comprehensive AI Knowledge: Understand AI fundamentals, including machine learning, deep learning, and natural language processing.
Advanced Prompt Engineering: Master key principles and advanced techniques to craft effective prompts and troubleshoot issues.
Practical AI Tools and Models: Gain hands-on experience with cutting-edge AI tools, text, and image generation models like GPT-4 and DALL-E 2.
Ethical AI Practices: Learn about AI ethics, including data security, privacy, and regulatory compliance to ensure responsible AI use.

Course Objectives

  • Understand the core concepts of Artificial Intelligence, Machine Learning, Deep Learning, and NLP
  • Apply effective prompt engineering principles to improve AI-generated responses
  • Utilize advanced prompting techniques such as Zero-Shot, Few-Shot, Chain-of-Thought, Prompt Chaining, and Retrieval Augmented Generation (RAG)
  • Explore and compare modern AI tools and models including ChatGPT, GPT-4, DALL·E, and emerging AI platforms
  • Create, optimize, and refine prompts for both text and image generation tasks
  • Develop practical AI-driven projects integrating text and image models
  • Evaluate AI response quality and improve output consistency and reliability
  • Understand ethical considerations, fairness, privacy, transparency, and governance in AI systems
  • Analyze future trends and emerging technologies in AI and Generative AI
  • Explore AI agents, their characteristics, and their applications in prompt engineering workflows

Course Outline 

Module 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

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

Optional Module: 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

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