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Learner Reviews & Feedback for Advanced Prompt Engineering for Everyone by Vanderbilt University

4.8
stars
596 ratings

About the Course

Unlock the full potential of generative AI and become a master of prompt engineering. Dive deeper into how you can use In-context Learning to build better and more reliable prompts. See how Retrieval Augmented Generation (RAG) works and what can go wrong that you can counteract with fact-checkable prompt formats. Overcome your struggles in getting the right output from generative AI models with template-based output formats to achieve precision in your interactions with AI models. Tap into powerful AI capabilities for tasks ranging from social media comment analysis to survey results interpretation and beyond. This course will empower you with the skills needed to build exceptional prompts, using simple techniques, such as preference-driven refinement, and become and expert in leveraging generative AI for productivity and creativity. What You Will Learn: In-Context Learning: Understand how to provide context within prompts to guide AI models towards more accurate and relevant outputs. Learn techniques to embed contextual information that enhances the model’s understanding and performance. Retrieval Augmented Generation (RAG): Explore how to integrate retrieval systems with generative models to provide more precise and informed responses. This module covers the mechanisms to combine the strengths of both retrieval and generation. Template Pattern and Examples: Master the art of crafting template-based prompts to achieve consistent and desired outputs. Learn how to tell generative AI to fit its output into your format and overcome struggles with getting exactly what you want. Tapping Into AI Capabilities for Everyday Use: Delve into practical applications such as analyzing social media comments, interpreting survey results, and other everyday tasks where generative AI can make a significant impact. Gain hands-on experience with real-world examples. Building Great Prompts: Discover simple methods for constructing effective prompts. Learn how preference-driven refinement can elevate your prompt engineering skills, ensuring that outputs meet your exact needs and preferences....

Top reviews

MP

Jul 30, 2024

Great course! You can always count on Jules White to learn about GenAI. Highly recommended. Looking forward to the next one.

CJ

Dec 15, 2025

The delivery of this course was clear, and the assignments allowed for a direct application of the material. Thank you for sharing your expertise, Professor Jules White.

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151 - 175 of 185 Reviews for Advanced Prompt Engineering for Everyone

By Leo K

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Jul 21, 2025

nice

By Gracie K

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Jun 3, 2025

wow!

By Rina M

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Apr 20, 2025

good

By Melisa Ç

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Sep 15, 2024

cool

By Mukul B

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Nov 12, 2025

NA

By Aakash K

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Sep 20, 2025

ok

By Maratov D M

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Apr 24, 2025

ok

By Owase A Z

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Feb 4, 2026

I

By StreamingAddict 1 (

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Jun 29, 2025

Not bad, but you would have had to worked with ChatGPT or an AI client or be taking a course in Large Language Models (LLM) to kind of understand what is going on.

By Heath A

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Aug 29, 2025

Solid overview and examples to make the most out of your prompt engineering techniques.

By Jeff Y

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Aug 12, 2024

The information presented on prompt components and markdown is very helpful.

By Shabir S D

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Jan 31, 2026

It was amazing to learn this course from this platform

By KONSTANTINOS G

•

Sep 16, 2024

Θα ήθελα να είναι μεταφρασμένο στα Ελληνικά

By Mr M S D

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Feb 4, 2026

Very nice course,,, learnt a lot

By Dr S D

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Feb 5, 2026

Great experience.nice topic

By Murray S

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Jan 20, 2025

Good overview of the topic.

By Rahul K

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Feb 14, 2026

Nice one very informative

By Bahadur S

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Feb 8, 2026

Advanced study material

By Sartaj A B

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Feb 5, 2026

Usefull information

By Rainer B

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Oct 11, 2024

very well explained

By Mohd A

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Feb 26, 2026

Good for knowledge

By Oleksandr Z

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Dec 28, 2024

Not deep enough

By UmamaheswaraRao P

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Sep 2, 2024

awesome course

By Mohammad A Z

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Jan 30, 2026

It was nice

By Feroz A K

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Feb 4, 2026

Too long