Analog circuits are the unsung heroes of our electronic world, translating real-world signals like sound and light into digital information. Designing these circuits is a complex task, traditionally relying on the expertise of seasoned engineers. But what if we could automate this process? Researchers are exploring the fascinating potential of Large Language Models (LLMs), the same technology behind chatbots like ChatGPT, to design analog circuits. A new approach, called AnalogXpert, uses LLMs not just to generate circuit designs, but to mimic the *reasoning* of expert circuit designers. Imagine an LLM that doesn't just connect components randomly, but understands how different subcircuits contribute to the overall function. That's the core idea behind AnalogXpert. It uses a specialized "subcircuit library," a collection of pre-designed circuit building blocks, much like an experienced engineer would. The LLM learns to select and connect these blocks based on specific design requirements, following a logical, step-by-step process guided by the principles of Chain-of-Thought prompting. Even more impressively, AnalogXpert incorporates a "proofreading" strategy. Just as human designers check their work for errors, the LLM iteratively refines its design based on feedback from a rule-based checker. This feedback loop helps the LLM learn from its mistakes and generate increasingly accurate and efficient circuit topologies. Tested on a benchmark of real-world and synthetic circuit design problems, AnalogXpert significantly outperformed standard LLMs, demonstrating the power of incorporating domain-specific knowledge and expert reasoning into AI design tools. While still in its early stages, this research suggests a future where LLMs could revolutionize analog circuit design, accelerating innovation and enabling the creation of more complex and sophisticated electronic systems. The next step? Expanding the real-world dataset and potentially fine-tuning smaller, specialized AI models for local use in commercial settings, ensuring data security while democratizing access to advanced design tools.