Move beyond writing prompts to engineering them. Build evals and rubrics to measure prompt quality objectively, use AI to write and refine your prompts, get reliable structured outputs, and run optimization loops that cut variance, cost, and latency.
This free Advanced Prompt Engineering course is for people who already know the basics of prompting and are tired of improving prompts by guesswork. Instead of adding more tricks, it teaches the discipline that makes prompts reliable: measuring quality objectively, then optimizing against that measurement. If you have ever shipped a prompt that worked on one example and broke on the next ten, this is the course that fixes the habit.
Across ten short lessons you will learn to build an evaluation set of realistic test cases, write rubrics that produce reproducible scores, and automate grading with a controlled LLM-as-judge while avoiding its well-known biases. You will use meta-prompting to let AI draft and refine your prompts, run tight refinement loops that improve a prompt from its own failures, and get dependable structured outputs with schema enforcement, validation, and graceful recovery when the model slips. The final module covers optimization at scale: A/B comparing prompts, reducing run-to-run variance, and engineering the tradeoff between quality, cost, and latency.
Everything is no-code and broadly applicable, not a developer career track, so the techniques work whether you apply AI to analysis, writing, operations, or research. The course is 100% free and includes a free certificate of completion you can add to your resume or LinkedIn profile.
4 modules • 10 lessons
Finish every lesson and pass the final exam to earn this free, shareable certificate.
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June 15, 2026
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It is for people who already know the basics of prompting and want to make their prompts reliable, not just clever. It is intermediate level and no-code, so it suits analysts, writers, operators, researchers, and anyone applying AI to real work.
No. Every technique, including building evals, using LLM-as-judge, and getting structured outputs, is taught so you can apply it in a chat window, a spreadsheet, or a no-code tool. The principles matter more than the tooling.
The beginner course teaches how to write a good prompt. This course teaches how to engineer one: measure its quality with evals and rubrics, optimize it with refinement loops and A/B comparison, and make its outputs reliable enough to build on.
You will be able to measure whether a prompt change actually helped, automate evaluation, use AI to improve your prompts, produce dependable structured outputs with validation, and balance quality against cost and latency.
Yes. The course is 100% free with no paywall, and you earn a free certificate of completion you can add to your resume or LinkedIn profile.
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