AI+ Prompt Engineer™

Master AI Prompts: Elevate Your Engineering Skills

The AI+ Prompt Engineer Certification Program introduces learners from diverse backgrounds and levels of expertise to the fundamental principles of artificial intelligence and prompts engineering. Covering the history, concepts, and applications of AI, machine learning, deep learning, neural networks, and natural language processing, the program also delves into best practices for designing effective prompts that harness the capabilities of AI models to their fullest potential. Through a combination of theoretical instruction and practical exercises, including project-based learning sessions, participants acquire the skills needed to create and utilize prompts across various domains and objectives.

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  • Basic knowledge of AI concepts and applications for understanding advanced topics.
  • Familiarity with Programming Languages such as Python or R.
  • Proficiency in Data Analysis and Interpretation.
  • Knowledge of Machine Learning Algorithms and Techniques.
  • Awareness of Ethical Issues and Considerations in AI Development.

Exam Details



50 MCQs 90 Minutes

Passing Score70%

Certification Modules

  1. 1.1 Introduction to Artificial Intelligence
  2. 1.2 History of AI
  3. 1.3 Machine Learning Basics
  4. 1.4 Deep Learning and Neural Networks
  5. 1.5 Natural Language Processing (NLP)
  6. 1.6 Prompt Engineering Fundamentals
  1. 2.1 Introduction to the Principles of Effective Prompting
  2. 2.2 Giving Directions
  3. 2.3 Formatting Responses
  4. 2.4 Providing Examples
  5. 2.5 Evaluating Response Quality
  6. 2.6 Dividing Labor
  7. 2.7 Applying The Five Principles
  8. 2.8 Fixing Failing Prompts
  1. 3.1 Understanding AI Tools and Models
  2. 3.2 Deep Dive into ChatGPT
  3. 3.3 Exploring GPT-4
  4. 3.4 Revolutionizing Art with DALL-E 2
  5. 3.5 Introduction to Emerging Tools using GPT
  6. 3.6 Specialized AI Models
  7. 3.7 Advanced AI Models
  8. 3.8 Google AI Innovations
  9. 3.9 Comparative Analysis of AI Tools
  10. 3.10 Practical Application Scenarios
  11. 3.11 Harnessing AI’s Potential
  1. 4.1 Zero-Shot Prompting
  2. 4.2 Few-Shot Prompting
  3. 4.3 Chain-of-Thought Prompting
  4. 4.4 Ensuring Self-Consistency in AI Responses
  5. 4.5 Generate Knowledge Prompting
  6. 4.6 Prompt Chaining
  7. 4.7 Tree of Thoughts: Exploring Multiple Solutions
  8. 4.8 Retrieval Augmented Generation
  9. 4.9 Graph Prompting and Advanced Data Interpretation
  10. 4.10 Application in Practice: Real-Life Scenarios
  11. 4.11 Practical Exercises
  1. 5.1 Introduction to Image Models
  2. 5.2 Understanding Image Generation
  3. 5.3 Style Modifiers and Quality Boosters in Image Generation
  4. 5.4 Advanced Prompt Engineering in AI Image Generation
  5. 5.5 Prompt Rewriting for Image Models
  6. 5.6 Image Modification Techniques: Inpainting and Outpainting
  7. 5.7 Realistic Image Generation
  8. 5.8 Realistic Models and Consistent Characters
  9. 5.9 Practical Application of Image Model Techniques
  1. 6.1 Introduction to Project-Based Learning in AI
  2. 6.2 Selecting a Project Theme
  3. 6.3 Project Planning and Design in AI
  4. 6.4 AI Implementation and Prompt Engineering
  5. 6.5 Integrating Text and Image Models
  6. 6.6 Evaluation and Integration in AI Projects
  7. 6.7 Engaging and Effective Project Presentation
  8. 6.8 Guided Project Example
  1. 7.1 Introduction to AI Ethics
  2. 7.2 Bias and Fairness in AI Models
  3. 7.3 Privacy and Data Security in AI
  4. 7.4 The Imperative for Transparency in AI Operations
  5. 7.5 Sustainable AI Development: An Imperative for the Future
  6. 7.6 Ethical Scenario Analysis in AI: Navigating the Complex Landscape
  7. 7.7 Navigating the Complex Landscape of AI Regulations and Governance
  8. 7.8 Navigating the Regulatory Landscape: A Guide for AI Practitioners
  9. 7.9 Ethical Frameworks and Guidelines in AI Development

What Will You Learn?

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Prompt Engineering Mastery

Students will learn how 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.

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Developing AI Architectural skills

Students will learn about AI tools and models used in prompt engineering and associated tasks. This includes AI architectures, algorithms, and frameworks to help them choose and implement AI solutions for varied applications.

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Mastering Picture Model Techniques

Participants 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.

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Project-Based Learning skills

Participants will master prompt engineering and AI concepts to real-world tasks. This includes implementing AI in many domains and situations through collaborative project work, improving their problem-solving, communication, and cooperation skills.

Industry Opportunities after Course Completion


The course combines theoretical instruction with practical exercises and project-based learning sessions. This structure helps participants gain both conceptual knowledge and hands-on experience.

This program is suitable for individuals from diverse backgrounds and levels of expertise who want to gain a comprehensive understanding of AI and prompt engineering. It is ideal for AI developers, data scientists, educators, and anyone interested in harnessing the full potential of AI models through effective prompting.

The program covers principles of effective prompting, such as giving clear directions, formatting responses, providing examples, evaluating response quality, and fixing failing prompts. Participants will learn to apply these principles through practical exercises and real-life scenarios.

Obtaining the AI+ Prompt Engineer certification demonstrates proficiency in AI and prompt engineering, enhancing personal credibility and professional development. It equips individuals with the skills needed to lead AI-driven initiatives, making them valuable assets in the rapidly evolving field of AI.

Practical exercises include project-based learning sessions where participants work on real-world problems, designing and implementing AI prompts to solve specific tasks.