RNA molecules, the unsung heroes of our cells, hold the keys to understanding life itself. Their intricate shapes, known as secondary structures, dictate how they function, influencing everything from gene expression to disease development. But deciphering these structures experimentally is a slow, costly process. Could AI offer a faster, more efficient way? A new study puts large language models (LLMs), the same technology powering AI chatbots, to the test in the complex world of RNA structure prediction. Researchers benchmarked six leading RNA-LLMs, analyzing their ability to predict these vital structures based solely on RNA sequences. These LLMs, trained on massive datasets of RNA data, learn to represent each RNA base with a rich numerical vector, capturing its contextual meaning. The hope is that these representations can unlock the secrets of RNA structure and function. The results reveal a mixed bag. While some LLMs, particularly the larger, more extensively trained models like RiNALMo and ERNIE-RNA, showed promising results, especially with shorter RNA sequences, they struggled with longer, more complex structures. Surprisingly, in some of the most challenging scenarios, a simple one-hot encoding method performed on par with the best LLMs. This suggests that even the most advanced AI models still have a lot to learn about the nuances of RNA folding. The study also highlighted the difficulty of cross-family prediction. When trained on one family of RNAs and tested on another, even the top-performing LLMs experienced significant performance drops. This underscores the vast diversity within the RNA world and the challenges of building truly generalizable AI models for structure prediction. However, the research provides a critical benchmark for future development. By identifying the strengths and weaknesses of current LLMs, the study paves the way for improved models that can more accurately and reliably predict RNA structures. This could revolutionize RNA research, accelerating our understanding of these essential molecules and their roles in health and disease. The ability to rapidly predict RNA structures could unlock new therapeutic targets and accelerate the development of RNA-based drugs and vaccines. While there's still work to be done, the potential of AI to decipher the RNA language of life is becoming increasingly clear.