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AI+ Prompt Engineer™
AC-130
Master AI Prompts: Elevate Your Engineering SkillsThe 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.
Buy Exam Bundle Download Blueprint Find a Training Partner Download Executive SummaryKey Benefits
The AI+ Prompt Engineer Certification Program provides essential skills and knowledge to master AI and prompt engineering. This seven-module course covers foundational AI concepts, advanced prompt engineering techniques, and practical applications, empowering you to generate actionable results effectively.
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.
Prerequisites
- 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
Modules
7
Examination
1
50 MCQs
90 Minutes
Passing Score
70%
Certification Modules
- Course Introduction Preview
- 1.1 Introduction to Artificial Intelligence
- 1.2 History of AI
- 1.3 Machine Learning Basics Preview
- 1.4 Deep Learning and Neural Networks
- 1.5 Natural Language Processing (NLP)
- 1.6 Prompt Engineering Fundamentals
- 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 QualityPreview
- 2.6 Dividing Labor
- 2.7 Applying The Five Principles
- 2.8 Fixing Failing Prompts
- 3.1 Understanding AI Tools and Models
- 3.2 Deep Dive into ChatGPT Preview
- 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
- 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 Preview
- 4.10 Application in Practice: Real-Life Scenarios
- 4.11 Practical Exercises
- 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
- 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
- 7.1 Introduction to AI Ethics Preview
- 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
Certification Modules
- Course Introduction Preview
- 1.1 Introduction to Artificial Intelligence
- 1.2 History of AI
- 1.3 Machine Learning Basics Preview
- 1.4 Deep Learning and Neural Networks
- 1.5 Natural Language Processing (NLP)
- 1.6 Prompt Engineering Fundamentals
- 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 QualityPreview
- 2.6 Dividing Labor
- 2.7 Applying The Five Principles
- 2.8 Fixing Failing Prompts
- 3.1 Understanding AI Tools and Models
- 3.2 Deep Dive into ChatGPT Preview
- 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
- 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 Preview
- 4.10 Application in Practice: Real-Life Scenarios
- 4.11 Practical Exercises
- 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
- 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
- 7.1 Introduction to AI Ethics Preview
- 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
Exam Objectives
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.
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.
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.
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.
Hear it from the Learners
Marc H
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Antonio C
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