AI+ Prompting Fundamentals™

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The AI+ Prompting Fundamentals™ certification provides a practical introduction to prompt engineering and the fundamentals of artificial intelligence.

The course covers AI concepts, effective prompting principles, leading AI tools and models, advanced prompting techniques, AI image generation, project-based learning and the ethical and regulatory considerations surrounding AI. Participants gain hands-on experience designing and optimising prompts, working with text and image generation models, and applying prompt engineering to real-world projects, making the certification suitable for both technical and non-technical professionals looking to use AI more effectively.

Duration

Self-Paced: Approximately 8 hours of on-demand content.
Includes on-demand video lessons, an e-book and podcasts, with modular quizzes to track progress.

Who Should Attend

Research Scientists: Use effective prompts to explore scientific data, support research and solve complex problems.
Data Scientists & Analysts: Apply prompt engineering to improve data analysis, machine learning workflows and the generation of useful insights.
Developers & Programmers: Learn to build, refine and deploy AI-driven applications using effective prompts.
Business Leaders & Strategists: Incorporate AI solutions into business strategies to optimise processes and support decision-making.
Machine Learning Engineers: Develop prompt engineering skills to improve the performance and effectiveness of AI and machine learning models.

Prerequisites

An understanding of basic AI concepts and how AI is used.
No technical skills are required.
A willingness to think creatively, generate ideas and use AI tools effectively.

Course Content

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
3.4 Revolutionizing Art with DALL-E
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
5.10 Ethical and Legal Dimensions of AI-Generated Images

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
6.9 Sample Projects and Evaluation Framework

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
7.10 Future of AI Governance and Responsible Innovation

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

Tools You’ll Explore

LangChain
OpenAI’s GPT

Why This Certification Matters

Comprehensive AI Knowledge: Develop an understanding of machine learning, deep learning, natural language processing and other AI fundamentals.
Advanced Prompt Engineering: Learn how to design effective prompts, evaluate AI responses and troubleshoot prompts that do not produce the desired results.
Practical AI Tools and Models: Gain hands-on experience with AI tools and text and image generation models, including GPT and DALL-E.
Ethical AI Practices: Understand issues such as data security, privacy, bias, transparency, sustainability and regulatory compliance.
Practical Application: Apply prompt engineering techniques through practical exercises and project-based learning to develop real-world AI solutions.

Skills You’ll Gain

Familiarity with Neural Networks
Basics of Natural Language Processing (NLP)
History and Concepts of AI
Designing Effective AI Prompts
Practical Application of Prompt Engineering
Project-Based Learning in AI Prompting

Exam Details

Duration: 90 minutes
Passing Score: 70% (35/50)
Format: 50 multiple-choice/multiple-response questions
Delivery Method: Online via a proctored exam platform with flexible scheduling.