Imagine sifting through billions of pieces of genetic code, searching for the hidden clues that determine everything from our ancestry to our predisposition to diseases. It's a monumental task, like finding a needle in a haystack the size of a planet. Traditionally, scientists have used data-driven methods to analyze this genetic data, but these methods often struggle when dealing with the sheer volume and complexity of the information. Now, a groundbreaking new approach is using the power of large language models (LLMs), the same technology behind AI chatbots, to revolutionize how we analyze genotypes. Researchers have developed a framework called FREEFORM, which leverages the knowledge embedded within LLMs to intelligently select and engineer genetic features. Think of it as giving the AI a detective's toolkit, enabling it to sift through the genetic data with unprecedented precision. Tested on real genetic datasets related to ancestry and hearing loss, FREEFORM outperformed traditional methods, especially when limited data was available. This is particularly crucial for rare genetic conditions where large datasets are often scarce. FREEFORM's ability to work effectively with smaller datasets opens exciting possibilities for personalized medicine and faster diagnosis. This breakthrough isn't just about improving prediction accuracy; it's about understanding the "why" behind the predictions. By focusing on interpretable interaction terms, FREEFORM provides insights into how different genetic variants work together to influence traits. This deeper understanding can pave the way for the development of targeted therapies and interventions. While the potential of this technology is immense, challenges remain. Further research is needed to refine the feature engineering process, explore different ways to integrate external knowledge sources, and enhance the interpretability of the AI's findings. As LLMs continue to evolve, we can expect even more powerful tools for unlocking the secrets hidden within our DNA, ushering in a new era of personalized medicine and genetic discovery.