Using ChatGPT for load testing in cross-platform apps presents significant hurdles primarily because it generates text, not executable code or direct API requests. A major challenge lies in translating AI-generated user scenarios into precise, platform-specific API calls and data payloads required by different environments like web, iOS, and Android. Furthermore, ensuring accurate simulation of diverse user interactions across varied UIs demands complex integration and custom scripting beyond raw text output. Another concern involves the cost and rate limits of LLM APIs when scaling up to thousands or millions of virtual users for realistic load scenarios. The non-deterministic nature of ChatGPT's output also makes it difficult to achieve reproducible test conditions, crucial for consistent performance analysis. Effectively leveraging ChatGPT requires extensive engineering to parse outputs, map them to app logic, and manage platform-specific nuances, making it less of an out-of-the-box solution. More details: https://enews2.sfera.net/newsletter/redirect.php?id=sabricattani@gmail.com_0000006566_144&link=https://abcname.com.ua/