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.
This module, "What is Prompt Engineering?", serves as an introductory exploration into the field of prompt engineering. It aims to provide learners with a foundational understanding of what prompt engineering entails, its significance in the realm of artificial intelligence, and how it is applied to optimize interactions with AI models.
Understand what prompt engineering is and recognize its significance in optimizing interactions with AI models.
A beginner-friendly explanation of how large language models (like GPT) function. This section breaks down complex concepts into simple terms to give learners a mental model of how AI generates responses.
Participants will be able to describe in simple terms what a large language model is and how GPT processes prompts to produce outputs.
A breakdown of the core components that make up a strong prompt—such as instructions, context, format, and examples. Students learn how these elements influence the quality and accuracy of AI responses.
Participants will be able to identify and apply the key elements of an effective prompt to guide AI outputs.
An exploration of system prompts—hidden or initial instructions that define how an AI assistant behaves. Students will see how tone, style, and personality can be shaped by these underlying prompts.
Participants will be able to explain what system prompts are and use them to establish rules, tone, or roles for AI interactions.
A practical look at how to refine prompts step by step to improve responses. Students will learn the value of experimentation and feedback loops when working with LLMs.
Participants will be able to apply iterative prompting techniques to progressively refine and enhance AI outputs.
An introduction to prompting strategies that encourage the AI to “show its reasoning” by generating step-by-step explanations before reaching an answer.
Participants will be able to design prompts that encourage structured reasoning and problem-solving using chain-of-thought techniques.
A comparison of three key prompting strategies: zero-shot (no examples), one-shot (a single example), and few-shot (multiple examples). Students learn when and how to apply each method.
Participants will be able to distinguish between zero-shot, one-shot, and few-shot prompting, and apply each approach to real-world tasks.