This course will train learners to utilise the ‘CLEAR’ Model. It is a practical, step-by-step framework designed to help you communicate effectively with AI systems, especially large language models like ChatGPT. With CLEAR, you’ll learn how to craft prompts that guide AI to provide accurate, relevant, and useful responses. More than just a simple tool, the CLEAR Model offers a complete approach to understanding and interacting with AI, making your prompts clear, targeted, and results-oriented.

Course Objectives:

Learn AI Fundamentals: Get a solid introduction to artificial intelligence, large language models (LLMs), and how they’re used in various fields.

Master Prompt Engineering with the CLEAR Model: Discover the essentials of prompt engineering and understand each part of the CLEAR Model: Clear Instructions, Level of Output, Example, Acting Role, and Result format. You’ll gain techniques to create powerful prompts that produce the responses you need.

Practice Through Real-World Scenarios: Apply the CLEAR Model with hands-on exercises and case studies to see how it works in real-life situations.

Build Digital Skills for Better AI Communication: Strengthen your digital skills and learn to communicate clearly with AI systems, improving the quality of outputs through thoughtful and well-structured prompts.

Final Evaluation: The course concludes with a comprehensive 20 multiple-choice question assessment

By the end of this course, you’ll be fully equipped with the skills and confidence to make your interactions with AI efficient and impactful

1. Introduction to Artificial Intelligence and Large Language Models (LLMs)
1.1 Understanding AI and its Impact on Society
1.2 Overview of Large Language Models (LLMs) and their Applications
1.3 Introduction to Artificial General Intelligence (AGI) and Future Possibilities
2. Introduction to Prompt Engineering
2.1 What is Prompt Engineering and its Role in AI Interactions
2.2 Examples of Prompt Engineering in Various Industries
2.3 Shaping the Future of AI with Effective Prompt Engineering
3. Understanding the CLEAR Model
3.1 Overview and Rationale of the CLEAR Model
3.2 Key Principles and Components of the CLEAR Model
3.3 Benefits and significance of applying the CLEAR Model
4. The CLEAR Model
C – Clear Instructions, Command, and Context
4.1 Exploring the importance of clear Instructions in Prompt Engineering
4.2 Techniques for crafting effective commands in AI prompts
4.3 Adapting Prompts to different contexts and scenarios
L – Level of Output Required
4.4 Understanding the Role of output level in Prompt engineering
4.5 Determining and communicating desired output levels
4.6 Adapting output levels for specific use cases
E – Example
4.7 Incorporating example outputs to guide AI models
4.8 Constructing effective examples for various prompting situations
4.9 Leveraging examples to improve Prompting results

A – Acting Role
4.10 The Role of Acting in Prompt Engineering
4.11 Techniques for assuming Acting Roles in AI interactions
4.12 Enhancing Prompting Outcomes Through Effective Acting
R – Result Format and Parameters
4.13 Understanding Result format and parameters in AI outputs
4.14 Techniques for Specifying Result format and parameters in Prompts
4.15 Customising Result format and parameters for optimal Results
5. Applying the CLEAR Model in Practice
5.1 Case Studies: Applying the CLEAR Model in real-world scenarios
5.2 Hands-on Exercises: Creating Prompts using the CLEAR Model
5.3 Best Practices and Tips for Implementing the CLEAR Model
6. Advanced Techniques in Prompt Engineering
6.1 Advanced Strategies for Clear Instructions, Command, and Context
6.2 Fine-tuning output levels, Examples, and Acting Roles
6.3 Navigating complex Result formats and parameters
7. Evaluation and Feedback in Prompt Engineering
7.1 Importance of evaluation and feedback in Prompting
7.2 Methods for evaluating Prompting effectiveness
7.3 Collecting and utilising feedback to improve Prompting skills
8. Future Trends in Prompt Engineering
8.1 Exploring Emerging Trends in Prompt Engineering and AI
8.2 Reflecting on digital skills development and future applications

9. Ethical Considerations in Prompt Engineering
9.1 Introduction to Ethics in AI
9.2 Bias and fairness in Prompt Engineering
9.3 Privacy and Data Protection
10. Continuous Professional Development (CPD) in Prompt Engineering
10.1 CPD in Prompt Engineering
10.2 Continuous Improvement and Reflective Practice
10.3 Future Trends in Prompt Engineering