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If you're trying to make sense of how AI fits into software testing, I strongly recommend starting with Hung Nguyen's Shade of Hue courses. I am a decades-long practitioner of software testing. It is hard to keep up with relevant AI updates while slogging through lots of amateur, poorly explained hype. On my current contract I used Hung's approach that I learned in his Testing AI Systems and Testing with AI class, and my client was most impressed with the tools and insights. In the past when I managed software testing teams, sending my team to these classes would have been a godsend. As a former adjunct Computer Science professor, I see in Hung Nguyen's Shade of Hue courses the underpinnings of organization and description from someone who knows how to break down complex subjects and refine them into practical concepts. He is a pro. All in all, I give the courses a strong recommendation.

I really enjoyed the Pragmatic Software Test Engineer course, and the most valuable part for me was its practical testing mindset. The course helped me understand that testing isn't about running enough tests, but about finding the important bugs, at the right time, and at the lowest cost. I learned how to focus on risk, design fewer but more effective test cases, and understand the real role of a tester on a project.

The AI Testing course helped me learn how to apply AI to real-world projects. I was guided on how to write quality prompts and provide the right context so AI could produce more accurate and useful results. I was also introduced to methods for testing AI systems, related tools, common AI errors, and the Black-Box Exploratory AI Testing method. In particular, after each module there were exercises that helped me review and reinforce my knowledge. My work was reviewed in detail by the SOH instructor, who gave specific feedback, helping me recognize my strengths, areas to improve, and clearly develop my skills.

The [Testing AI Systems and Testing with AI] course was quite interesting, as it introduced a new area of software testing in the evolving field of information technology, especially given the rapid development of artificial intelligence (AI). AI brings many benefits and positive changes to our lives; however, it also comes with potential risks and negative impacts. Therefore, quality assurance and appropriate controls are essential to prevent potential misuse and harmful behaviors.

After 25 years of leading testing projects, I wanted to see how AI changes the work, and the Testing AI Systems and Testing with AI course gave me a practical way to bring it into the testing I already knew. It trained me to use AI as a practical testing partner while developing the skills to evaluate AI-powered systems for reliability, fairness, robustness, explainability, and consistency.

The most valuable part of the course for me was that it helped me understand manual testing [Pragmatic Software Test Engineer] more clearly and systematically. Before, I knew the basics, but after the course, I understood why we do each step in manual testing, not just how to do it. The AI part [Testing AI Systems and Testing with AI] was also very useful. I learned how to use AI by writing proper prompts to support my testing work instead of depending on it completely. I also understood how AI uses data and why, as a tester, I still need to review, justify, and evaluate AI outputs to make sure they are correct and reliable. Overall, the course helped me become more confident in my testing knowledge and more careful when using AI in real testing scenarios.

In early 2025, Hung led a two-month AI workshop for our US team at LogiGear. It focused on both testing AI systems and testing with AI, and it included people from outside testing and QA, since we wanted everyone to get ready and get ahead by using AI workflows in their work. Hung made it practical. People came away using AI in their own jobs, not just talking about it. For a company leading in AI testing, that's exactly how we keep our whole team ahead.

Our students come to PNV with talent and drive, and leave with life-changing opportunities. Shade of Hue's training gives them practical testing skills, including how to test AI systems and work with AI, during their three-year program. Some of our graduates have gone straight into AI testing jobs. For young people building a career in tech, that is real, lasting impact.

I've built engineering teams for most of my career, and I've seen what happens when testing is an afterthought. Hung has spent his career making the opposite case: testing is engineering work that takes real thinking, and testers who understand the product, the technologies, and the risk are worth their weight. What I like about Shade of Hue is that it stays practical and treats learning as a journey, not a destination. It teaches testing practitioners how to work with AI without handing their judgment over to it. Teams that take this approach will ship better software.

In the era of artificial intelligence (AI), developing practical technology skills is essential to building a highly competent workforce. Shade of Hue provides software testing professionals and learners with a body of hands-on knowledge, including methods for testing AI systems and the effective application of AI, while ensuring that human judgment and decision-making remain firmly in place. With a practical, forward-looking approach, Shade of Hue makes a significant contribution to developing the technology workforce in Huế City and other localities.

I'm not a tester or a software developer, but I build tools for my research. The self-paced AI testing course has helped me test those tools far more effectively. I especially appreciate the play area, where I can practice what I'm learning and build confidence through hands-on experience. Shadeofhue has made it easier for me to develop and test software for my research.

After completing the Testing AI Systems and Testing with AI course, I changed how I think about using AI to support my learning: AI can do 80% of the work, but the user still needs to evaluate and refine the remaining 20%. This was my first time taking a video-based testing course, and I found the content engaging. The course also provided learning support tools such as NotebookLM and the SOH ChatGPT.

A clear, well-organized guide to the bugs that only AI systems have. It breaks hallucinations into seven types, from factual and logical to bias-driven and multi-turn, and gives a concrete way to test for each one. Then it covers how people can deliberately trick AI models. A great checklist for any tester moving into testing AI.