The Basics of Prompt Engineering

What is Prompt Engineering

Key LLM Concepts

Basic Tips

Traditional vs Reasoning Models

Anatomy of a Prompt

Traditional Model Example

Prompting Reasoning Models

Reasoning Model Example

Introduction

In this course, we teach you how to write effective prompts for Large Language Models (LLMs)—and why prompt engineering is quickly becoming one of the most valuable skills in the AI age. LLMs are powerful tools for reasoning, summarizing, generating, and coding, but their outputs are only as good as the prompts they’re given. Most people waste hours tweaking inputs without understanding why the model behaves the way it does. This course fixes that.

In just under two hours, we cover the foundational mechanics of how LLMs interpret prompts, the anatomy of a well-structured prompt, and how to adapt your approach depending on whether you're using traditional or reasoning models. You'll see real examples where we walk through prompts live, showing both what works and what breaks—and how to fix it.

It’s short, high-signal, and comes with copy-paste prompt templates, annotated examples, and real-world use cases you can apply immediately in your own projects—whether you're writing summaries, generating structured data, or integrating LLMs into your product stack.

It’s taught by Nick, a senior QA engineer and technical project manager who worked on Alexa at Amazon and has spent years translating abstract AI concepts into production-ready workflows. He’s taught thousands of students and consulted with teams deploying LLMs into everything from APIs to internal tools.

You don’t need to know code. You don’t need to understand math. You just need to understand the logic behind the prompt—and that’s what this course is built to teach you.

Whether you’re a developer, product manager, content strategist, or educator, prompt engineering is a core literacy for the new wave of AI tools. And this is the clearest, fastest way to get started.

Get Consistent Results from LLMs

In this course, we'll take you through the basics of prompt engineering - looking at some high level concepts for prompting LLMs, the difference between traditional and reasoning models, and best practices for prompting both to ensure consistant results.

Understand how LLMs interpret inputs by learning tokenization, context windows, and output limits to write more predictable, cost-efficient prompts.

Craft well-structured prompts using a clear framework of task, detail, tone, format, and context to reduce ambiguity and improve output quality.

Select the right model for the job by distinguishing when to use traditional vs. reasoning models based on task complexity and performance tradeoffs.

Improve output consistency and reduce hallucinations by applying structured formatting, clear language, and example-driven prompting techniques.

Watch real-world prompt demos through side-by-side examples that show how different models process the same task and why prompt design matters.

Build AI-native communication skills that are becoming essential across product, engineering, content, and research roles in the AI-driven workplace.

This module introduces the core idea behind prompt engineering—less a technical discipline and more a communication craft. You’ll explore what it truly means to “engineer” prompts, and why the term persists despite some debate in the technical community.

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title: What is Prompt Engineering
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### What is Prompt Engineering?

This module introduces the core idea behind prompt engineering—less a technical discipline and more a communication craft. You’ll explore what it truly means to “engineer” prompts, and why the term persists despite some debate in the technical community.

Nick Badot

Okay, let's move on. So what is prompt engineering? You guys probably already

Generally, I figure if you ask this question to people, especially in technical

there's two answers you're going to get, right? The first answer is usually

actually engineering, but it's a buzzword, which honestly, it is more about

engineering, right? You don't need to be able to code or even to weld something

prompt, right? So it is really more in the sphere of communications than the

So I won't even fully disagree with people that say this, but this is a nomencl

around. So that's what we're calling it, because it's the easiest thing to

right? But effectively, all it is writing the right prompts to get the desired

algorithms and other gen AI tools. So this applies to pretty much any non-deter

language model that you're using, right? Because when you're in that non-deter

there's a lot of room for ambiguity, there's a lot of room for error, there's

of course. Good prompting tries to anticipate those things get you some kind of