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.

The Basics of Prompt Engineering

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title: Traditional vs Reasoning Models
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Large Language Models (LLMs) are not all built the same. Understanding the difference between traditional and reasoning models is essential for selecting the right tool—and prompt style—for your task. This module breaks down how these two model types work, why their behavior differs, and when each is appropriate.

# Traditional Models

Traditional LLMs operate on a straightforward principle: they take a prompt, process it once, and return a single prediction, token by token. The process is linear and probabilistic—words are generated based on the most statistically likely next token.

These models excel in:

    Fast responses

    Lower computational cost

    Simple generation tasks (e.g., summaries, rewrites, completions)

They’re ideal when speed, simplicity, and cost are priorities—and when the task doesn’t require deep logical reasoning or intermediate steps.