Large language models (LLMs) are revolutionizing AI, but their inner workings remain mysterious. How do these complex systems, with billions of parameters, actually function? A groundbreaking new study uses a surprising technique borrowed from biology—mutagenesis—to shed light on this AI puzzle. Researchers tweaked parameters in two leading LLMs, Llama 2 and Zephyr, pushing values to their extremes to observe the effects. The results were remarkable. They found that even small changes to certain parameters could dramatically alter the model's behavior, sometimes for the better, sometimes for the worse. Some tweaks led to unexpected creative outputs like poetry and dialogue, while others hindered performance on standard tasks. These findings suggest that LLMs possess hidden structures that govern their capabilities. By understanding these structures, we can unlock new levels of control and potentially discover entirely new applications for LLMs. While this research offers a fascinating glimpse into the complexities of LLMs, it also highlights the need for further investigation. Developing more refined methods to analyze these systems will be crucial for harnessing their full potential and ensuring they are used responsibly. This mutagenesis approach, bridging biology and AI, represents a significant step towards demystifying LLMs and paving the way for a future of more powerful and creative AI systems.