Imagine searching a vast digital library, not page by page, but by instantly grasping the most relevant information. That's the potential of Large Language Models (LLMs) in search. But traditional methods can be slow, like carefully reading every title before picking the best book. A new research paper, "FIRST: Faster Improved Listwise Reranking with Single Token Decoding," introduces a breakthrough technique. Think of it as judging a book by its cover, but with AI precision. This method, called FIRST, uses the initial "logit"—a prediction score—to instantly rank search results. It's like having an AI librarian who whispers the best choices without having to scan the entire shelf. Instead of generating a complete ranked list, FIRST analyzes the model's first prediction about the top candidate, instantly revealing the relative importance of all the options. This is not only 50% faster but, surprisingly, maintains accuracy. How? By incorporating a learning-to-rank loss during training, prioritizing accuracy for the most important results. It's like training the AI librarian to focus on the bestsellers, not just the entire collection. This method also enhances other aspects of search. Imagine refining your search based on the initial results, and the AI instantly understands your needs and suggests even more relevant information. FIRST allows LLMs to provide this type of powerful relevance feedback, enhancing the search experience in ways not possible before. While FIRST shows incredible promise, challenges remain. Further research is needed to explore its full potential with human-annotated data and expand its capabilities to other languages. But one thing is clear: FIRST opens up a whole new chapter in the story of AI-powered search.