Creating flawless hardware is a complex process, and rigorous testing is essential. Traditionally, creating tests for hardware designs has been a manual, time-consuming task for engineers. But what if AI could take over? New research explores using Large Language Models (LLMs), like those powering ChatGPT, to automatically generate these tests. The approach involves using the LLM as a "Verilog Reader," allowing it to understand the hardware design code (written in Verilog) and identify areas needing testing. The researchers created a framework called VerilogReader that feeds the LLM information about the hardware design and code coverage—how much of the code has been tested. The LLM then generates test inputs aimed at covering untested parts of the code. In experiments comparing this AI-driven approach to traditional random testing, the LLM-powered system significantly outperformed random tests in simpler designs, achieving 100% code coverage much faster. This suggests LLMs could dramatically speed up and improve the hardware verification process. However, challenges remain. The research also showed that LLMs struggle with larger, more complex hardware designs. Their ability to understand the code and infer the necessary tests diminishes as the design scales up. Future research aims to overcome this limitation by giving the LLM a higher-level understanding of the design and perhaps combining LLMs with other AI techniques specializing in structural analysis. While there’s still work to be done, this research hints at a future where AI plays a vital role in ensuring the reliability and robustness of hardware designs.