Imagine unlocking the secrets of individual cells, understanding their unique roles, and pinpointing the origins of diseases. This isn't science fiction, but the potential of single-cell genomics. Now, Artificial Intelligence (AI), specifically Large Language Models (LLMs), are stepping into the arena, learning to annotate cell types with remarkable accuracy. Researchers have developed a new benchmark called SOAR (Single-Cell Omics Arena) to test how well these LLMs can identify cells based on their unique molecular profiles. Think of it like giving an AI a puzzle where the pieces are gene expression patterns. Surprisingly, these AI models, trained primarily on text, perform exceptionally well, often rivaling or even surpassing specialized AI models designed for biological data. The trick? Using a technique called 'chain-of-thought prompting,' where the AI is encouraged to 'think' step-by-step through the data, much like a human expert would. This method significantly boosts their performance, proving particularly useful in multi-omics analysis where data from different biological layers are combined. SOAR isn't just about testing AI; it's about unlocking new possibilities in genomics research. By automating cell type annotation, we can accelerate discoveries, uncover hidden connections between cells, and pave the way for personalized medicine tailored to individual cellular profiles. While challenges remain, especially in handling the vast diversity of cellular data, this research shows the incredible potential of LLMs to decipher the complex language of our cells.