In a world increasingly reliant on voice-activated devices, ensuring the quality and security of Voice Personal Assistant (VPA) apps is paramount. Imagine a scenario where your smart speaker misunderstands your requests, or worse, leaks your private information. This is where rigorous VPA app testing comes in. Traditional testing methods, however, struggle to keep up with the complexity and sheer number of these apps, like the 200,000+ skills available for Amazon Alexa alone. They often lack the ability to truly *understand* the conversational flow and context within these apps. Now, a new approach is emerging: using Large Language Models (LLMs), like those powering ChatGPT, to supercharge VPA testing. Researchers have developed a framework called Elevate, which leverages the language understanding capabilities of LLMs to build smarter testing models. These models can interpret the nuances of human-like conversations, extract key information, and generate targeted test inputs. Instead of simply throwing random inputs at an app, Elevate can anticipate potential issues and explore the app's behavior more thoroughly. In tests with real Alexa skills, Elevate achieved significantly better coverage than traditional methods, uncovering more potential problems in less time. This innovative approach not only boosts efficiency but also helps developers ensure their apps are secure, reliable, and truly user-friendly. While still reliant on the underlying LLM technology and its inherent limitations, Elevate shows the potential of combining the strengths of model-based testing with the natural language prowess of LLMs. This exciting development paves the way for more robust and trustworthy VPA apps in the future, ultimately leading to a more seamless and secure voice-activated experience for everyone.