Imagine classifying images without any prior training. That's the promise of zero-shot image classification, a cutting-edge field in computer vision. Traditionally, this task has relied on models like CLIP, which learn to match images and text descriptions. But what if we could enhance this process by giving these models a deeper understanding of what they "see"? New research explores exactly that, using the power of multimodal Large Language Models (LLMs) like Gemini Pro. These LLMs can generate rich textual descriptions of images, going beyond simple labels to capture intricate details and context. The research introduces a simple yet effective method. First, the LLM generates a detailed description of the input image and makes an initial guess at its class. These textual insights, along with the image itself, are then encoded into a shared feature space. Finally, a linear classifier uses these combined features to predict the image's class. The results are impressive. This method outperforms existing zero-shot methods on a variety of datasets, including a significant boost on the challenging ImageNet benchmark. This improvement comes from the LLM's ability to provide more nuanced information. For example, instead of just labeling an image as "cat," the LLM might describe it as "a fluffy gray cat curled up on a blue blanket." This richer context helps the classifier make more accurate predictions. While this approach shows great promise, it does have limitations. The reliance on LLMs can be computationally intensive, and the generated descriptions, while usually helpful, can sometimes mislead the classifier. However, as LLMs become more efficient and their understanding of images improves, this method could revolutionize how we approach image classification. The future of zero-shot learning looks bright, with multimodal LLMs leading the charge toward more intelligent and adaptable computer vision systems.