Imagine a world where product developers could access a treasure trove of user feedback instantly, effortlessly, and without costly surveys. This isn't science fiction, but the promise of a groundbreaking research project exploring the use of large language models (LLMs) to create synthetic datasets for product desirability testing. Researchers experimented with GPT-4o-mini, a cost-effective LLM, to generate thousands of realistic product reviews, simulating user sentiment toward hypothetical software. They explored several methods, each with unique approaches to prompting the LLM, and found that the AI could produce reviews highly aligned with pre-defined sentiment scores. This opens exciting possibilities for scaling user-centered design, as synthetic datasets can supplement or even replace traditional feedback methods when real-world data is scarce or expensive to collect. This could revolutionize product development by providing rapid, scalable insights into user preferences, ultimately leading to more desirable and user-friendly products. However, the AI wasn’t perfect. It showed a bias towards positive reviews and sometimes struggled to capture the nuances of mixed sentiment. The research also highlights important ethical considerations, such as mitigating potential biases embedded in the LLM and ensuring responsible use of synthetic data. This innovative work lays the groundwork for a future where AI empowers us to understand and respond to user needs more effectively than ever before.