Software design patterns are reusable solutions to common programming problems. Traditionally, these patterns are identified and documented by human experts through careful observation and analysis of successful software projects. But what if we could automate this process? A recent research paper explores using ChatGPT to mine these valuable patterns, potentially revolutionizing how we design and build software. The research proposes a collaborative eight-step process where human insight works hand-in-hand with ChatGPT's AI capabilities. The process starts with selecting real-world application examples showcasing different design solutions. Then, using carefully crafted prompts, ChatGPT is guided to identify recurring solutions, define the problems they address, and distill these problem-solution pairs into reusable patterns. The research goes further by suggesting the inclusion of 'affordances' – the inherent capabilities of software components – within the pattern descriptions. This helps to understand not just *what* a solution does, but *how* it leverages the underlying components. The research demonstrates this process by creating a pattern language for integrating Large Language Models (LLMs) with data sources and external tools. The experiment revealed that ChatGPT can indeed identify key patterns. For instance, it identified the need for custom application logic when integrating LLMs with diverse data sources and tools, highlighting the importance of tailoring solutions to specific application needs. Other patterns discovered include data preprocessing, semantic understanding and synthesis, and adaptive response generation. However, the research also emphasizes that ChatGPT's output isn't perfect. It requires a human expert to refine the generated patterns, clarify terminology, and add crucial context. Sometimes, ChatGPT generated overly generic patterns or used imprecise language. The study highlights the importance of this human-AI partnership, where the AI augments human expertise rather than replacing it. This research opens exciting possibilities. Imagine automatically generating pattern languages from vast codebases, accelerating software development and improving code quality. However, challenges remain. The process relies heavily on the quality of the input examples and the prompts used to guide ChatGPT. Future research could explore more sophisticated prompting techniques, alternative AI models, and methods for scaling the process to handle larger datasets. This initial exploration demonstrates the potential of using AI for pattern mining, suggesting a future where AI and humans collaborate to design and build better software.