Ever wonder how search engines seem to know what you mean, even when your search terms are a bit off? The secret lies in query reformulation, a powerful AI technique that transforms your initial search into something the search engine can truly understand. New research explores how "ensemble prompting" takes this a step further. Imagine having a team of AI assistants, each with a slightly different perspective, working together to refine your search. That's the essence of ensemble prompting. Researchers are experimenting with prompting large language models (LLMs) with multiple, paraphrased instructions for the same query. This generates a diverse set of keywords, which are then combined or "fused" to create a supercharged search query. The results are impressive, showing significant improvements in search relevance across various benchmarks. This technique not only helps in pre-retrieval (before the search engine even looks for results) but also in post-retrieval, using early results to further refine the search. By leveraging the collective intelligence of multiple LLMs, ensemble prompting unlocks a new level of search precision. This means more accurate results, faster discovery, and a smoother overall search experience. While challenges remain, such as managing increased processing time, this research opens exciting doors for the future of search. As LLMs become more sophisticated, we can expect even more intelligent and intuitive ways to find the information we need.