This introductory learning path on Prompt Engineering is designed to equip participants with the essential skills to effectively interact with large language models (LLMs) such as GPT. Through practical examples and freely available online resources, students will explore how prompts shape AI responses, the key elements that make a prompt effective, and strategies to refine prompts for better outcomes. Special attention is given to techniques like system prompts, iterative prompting, chain-of-thought reasoning, and different prompting paradigms (zero-shot, one-shot, few-shot).

By the end of this course, participants will be able to:

• Define prompt engineering and explain its role in working with LLMs.

• Describe in simple terms how large language models (like GPT) work.

• Identify and apply the key elements of a well-structured prompt.

• Use system prompts to set context and guide model behavior.

• Apply iterative prompting techniques to refine and improve AI outputs.

• Utilize chain-of-thought prompting to encourage structured reasoning.

• Differentiate between zero-shot, one-shot, and few-shot prompting, and apply each approach appropriately.