The Basics of Prompt Engineering

In just two hours, you will:

1. 1. Understand how LLMs process prompts (tokenization, context windows, output constraints).
2. 2. Learn how to create effective prompts using a clear structure (task, details, tone, format, context).
3. 3. Know when to use classic models and when to opt for reasoning models, based on task complexity.
4. 4. Discover how to reduce "hallucinations" and improve result stability using examples and formalized techniques.
5. 5. See actual demonstrations of prompts and compare their performance across different models.

The course is short, concentrated, and practical: you will receive ready-made prompt templates, annotated examples, and scenarios for application in real projects—from writing resumes and generating structured data to integrating LLM into products. The course is led by Nick, a senior QA engineer and technical project manager, who previously worked on Alexa at Amazon. He has trained thousands of students and advised teams on integrating AI into workflows—from APIs to internal tools.