Designing data acquisition systems is a complex task, traditionally requiring significant expertise. Imagine, however, being able to simply describe your project goals to an AI and having it generate a system diagram and component specifications. Researchers are exploring precisely this in a new study using GPTs. The study, titled "Chattronics: Using GPTs to Assist in the Design of Data Acquisition Systems," investigates whether large language models (LLMs) can handle the intricacies of designing these systems. The research focused on a novel approach: a conversational AI tool where users describe their project, and the GPT suggests a system architecture and block-level specifications using a top-down approach. To test this, researchers simulated different data acquisition projects, like measuring angular position, temperature, acceleration, and combined pressure/temperature. Two testing methods were employed: one where the AI directly received all project requirements, and another where a second GPT acted as a user, asking clarifying questions. The results? While promising, they also highlighted limitations. The AI successfully generated coherent system architectures and component suggestions in many cases. However, it sometimes struggled to simultaneously consider all requirements and occasionally made theoretical errors, like suggesting incorrect component values or amplifier topologies. This underscores that while AI can be a powerful assistant in system design, it's not yet a replacement for human expertise. One key challenge lies in the GPT's limited ability to maintain context over long conversations. The researchers found that the AI often made inconsistent design choices across different blocks within the same system. This suggests that future research should focus on improving AI's long-term memory and context retention. Another crucial insight was the difference between the two testing methods. When the AI received all requirements upfront, it generated more consistent designs. However, the interactive approach with a second GPT, while less consistent, provided valuable insights into how AI can actively engage in a design process by asking questions. This points to the potential for AI to become an interactive design partner, rather than just a passive tool. The future of AI in data acquisition system design hinges on addressing these limitations. Improving AI's ability to handle complex calculations, retain context, and interact effectively with users will pave the way for more robust and reliable AI-driven design tools.