Prompt Engineering: Working Smarter with Powerful Prompts

The Prompt Engineering Course is a comprehensive, practical training designed to teach professionals how to design, refine, and apply advanced prompt engineering techniques. This improves the performance, creativity, and decision-making capabilities of AI in real-world contexts. The course duration is approximately 8 hours and leads to the skill of designing, testing, and optimizing effective prompts that guide AI systems toward accurate, creative, and structured outputs.

What You Will Learn

• Understanding Prompt Engineering: You will learn how structured prompts shape AI-generated responses in various contexts.

• Refining AI Outputs: You will analyze and adjust prompts to improve the accuracy, clarity, and relevance of AI results.

• Mastering Advanced Prompting Techniques: You will apply advanced methods such as Chain of Thought (CoT) and Presence Penalty (PP) to optimize AI performance.

• Designing Structured Prompts: You will create customized prompts for creativity, storytelling, systematic problem-solving, and decision-making.

• Evaluating and Testing: You will critically evaluate outputs, use debugging strategies, and refine prompts through iterative methods like A/B testing and AI self-critique.

Who Is This Training For?

The training is intended for professionals in roles such as marketing, consulting, project management, Learning & Development (L&D), and IT who already work with AI tools (such as ChatGPT or Copilot). The course is suitable for anyone who wants more control over AI output, strives for consistent results, and aims to improve productivity through structured AI workflows.

Our Teaching Method

The teaching method is distinguished by an applied, feedback-driven approach, in contrast to purely theoretical online courses. The learning journey includes scenario-based exercises to directly apply the theory to real business and creative tasks.

Modules

• Welcome to Prompt Engineering! This chapter sets the expectations for the course and emphasizes why prompt engineering is a crucial, transferable professional skill. You will learn how to navigate the course structure and identify how the content aligns with your specific professional context.

• Foundations of Prompt Engineering: This module builds foundational knowledge of how Large Language Models (LLMs) generate responses using tokens and context windows. You will learn the essential elements of a strong prompt, such as clarity, specificity, tone, and context, and how to recognize the pitfalls of vague prompts.

• Setting Goals & Evaluating AI-Generated Output: You will learn how to define clear goals for your prompts that align with professional tasks, and how to evaluate AI outputs based on five criteria (relevance, tone, completeness, format, and utility). This chapter covers detecting bias or hallucinations and applying iteration methods like scaffolding and AI self-critique to refine the results.

• Practical Applications of AI in Creative Tasks: In this section, you will apply prompt engineering to generate creative outputs, such as campaign ideas or brainstorming lists, and to structure prompts for professional decision-making. You will identify ethical risks of creative AI use and apply prompting skills to real-world scenarios in marketing and consulting.

• Problem-Solving Reasoning with AI: This module focuses on using AI for structured reasoning tasks, such as generating hypotheses or scenario planning for product launches, for example. You will learn to apply troubleshooting prompts for root cause analysis and reflect on best practices for using AI in systematic reasoning.

• Multimodal and Structured Output Prompting: You will learn to design prompts that combine text and images for richer results, and how to generate reproducible output in structured formats such as tables, lists, or JSON. This is applied through data-driven prompting to explore, analyze, and visualize datasets in various case studies.

• Advanced Prompting Techniques: In this concluding chapter, you will apply advanced techniques such as Chain of Thought (CoT) and Automatic Prompt Engineering (APE) to increase the accuracy and coherence of AI outputs. You will also learn to adjust the Presence Penalty (PP) to manage the balance between consistency and creativity and reflect on the responsible use of these advanced tools.