Prompt Engineering for Real Work
Structured techniques for getting reliable, high-quality output from AI tools on real professional tasks — not just clever one-liners.
Who this course is for
Anyone who has used an AI chat tool, been unimpressed, and suspected the problem might be how they were asking. That suspicion is usually correct, and the gap between a disappointing answer and a useful one is smaller than most people expect. The course is aimed at practical everyday use rather than research or engineering, so the examples are emails, documents, and ordinary work tasks rather than benchmarks — and nothing in it depends on which AI tool you happen to use.
What you will learn
- A four-part structure that covers what most weak prompts leave out
- Why vague prompts reliably produce vague answers, and what specificity actually means in practice
- How to treat the first response as a draft and iterate deliberately instead of starting over
- The prompting mistakes that show up most often, and the specific fix for each one
Before you start
You should have used an AI chat tool at least once. Knowing how the models work helps but is not required — AI Fundamentals covers that if you want the background first.
Course contents
Structuring Requests That Actually Work
Working With AI Iteratively
Avoiding Common Mistakes
After this course
You will be able to look at a disappointing answer and identify what was missing from your request, which is the skill that separates people who find these tools useful from people who conclude they are overhyped. It also transfers: the reasoning works the same way in whatever tool replaces the one you use today. The Prompt Library gives you tested templates built on the same structure, each explaining why it is worded the way it is.