Creating testbenches, the bedrock of verifying digital circuits, is like creating an obstacle course to make sure a new race car can handle anything. It's a crucial process in chip design, but traditionally, it’s been partially manual, tedious, and time-consuming. Researchers are now exploring whether Large Language Models (LLMs) can automate this. Directly using LLMs like throwing a robot into the race car factory without instructions - they struggle to produce effective testbenches. Enter AutoBench, a new framework that uses LLMs strategically to generate self-checking testbenches automatically. Imagine giving the robot a step-by-step guide, starting with understanding what kind of car it’s testing (combinational or sequential circuit). AutoBench then has the LLM summarize the testing goals, outline different test scenarios, and write the code for both driving the circuit (applying inputs) and checking if the outputs are correct. It's like the robot is learning to both drive the car and inspect it afterwards. AutoBench employs a clever trick: it generates part of the testbench in Python, harnessing LLMs’ strength in software rather than just hardware code. AutoBench even includes a self-improvement system—a way for the robot to double-check its work. It looks for missing test cases and automatically debugs code errors, aiming for the highest possible verification coverage. Initial results are promising, showing a substantial improvement over directly using LLMs. For more complex sequential circuits, the improvement is even more dramatic. AutoBench isn't perfect yet. It relies on having a correct “golden” version of the circuit to compare against, which isn't always available in the real world. Future work may address this limitation by adding the ability to simulate real-world bugs and other testing scenarios. But even in its current form, AutoBench shows how LLMs can transform the complex world of chip design, paving the way for more automated and efficient design flows.