Formerly known as AI+ Prompt Engineer Level 1™

Master AI Prompts: Elevate Your Engineering Skills Foundational Knowledge: Covers generative AI, ML, NLP, and neural networks essentials Hands-on Learning: Offers practical training in designing and optimizing prompts Industry-Relevant Skills: Prepares learners to build effective AI solutions across sectors Prompting Expertise: Certifies participants to craft impactful, domain-specific prompts

• 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.

• Research Scientists: Advance your research with AI by creating and utilizing effective prompts to explore new scientific data and solve complex problems.
• Data Scientists & Analysts: Enhance your ability to optimize machine learning models by mastering prompt engineering for better data analysis and insights.
• Developers & Programmers: Learn to build, refine, and deploy AI-driven applications by creating efficient prompts for improved AI system performance.
• Business Leaders & Strategists: Gain the skills to incorporate AI solutions into business strategies, optimizing processes and decision-making.
• Machine Learning Engineers: Strengthen your expertise by learning how to fine-tune AI prompts to enhance the performance of machine learning models.

• Understand AI basics and how AI is used – no technical skills required.
• Willingness to think creatively to generate ideas and use AI tools effectively

Course Overview

Course Introduction

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
• 1. What Are AI Agents
• 2. Applications and Trends of AI Agents for Prompt Engineers
• 3. How Does an AI Agent Work
• 4. Core Characteristics of AI Agents
• 5. Importance of AI Agents
• 6. Types of AI Agents