Imagine typing "design a water bottle" and having a computer instantly create a 3D model ready for manufacturing. That's the promise of Query2CAD, a groundbreaking research project that's bridging the gap between natural language and computer-aided design (CAD). Traditionally, creating CAD models has been a time-consuming process requiring specialized software and expertise. Query2CAD changes this by using large language models (LLMs), like the ones powering ChatGPT, to generate CAD designs directly from text descriptions. It works by translating your text prompt into a Python macro, a small program that can be run within CAD software like FreeCAD. This macro then builds the 3D model. But what if the model isn't quite right? Query2CAD has a clever solution: it refines its designs through a feedback loop. It uses an image captioning model to describe the generated design and then compares it to your original text. If there's a mismatch, it tweaks the macro and tries again. This iterative process mimics how human designers work, refining their creations until they're perfect. In tests, Query2CAD achieved remarkable accuracy, especially for simpler designs. It successfully generated cubes, spheres, and even more complex shapes like basketball hoops with varying degrees of success. While the system performs best with powerful LLMs like GPT-4, the research shows the potential of using AI to democratize CAD design, making it accessible to anyone with a good idea and a text prompt. However, challenges remain, particularly with highly complex designs. Future research could explore incorporating sketches or images alongside text prompts to further enhance the system's capabilities. This research opens exciting possibilities for the future of design and manufacturing. Imagine architects designing buildings with simple voice commands or hobbyists creating custom 3D-printed objects from a quick text description. Query2CAD is a significant step towards a future where design is limited only by our imagination.