Large Language Models (LLMs) are rapidly changing how we search for and find information. They're increasingly used to "rerank" search results, ensuring the most relevant ones appear at the top. But there's a hidden human element: prompt engineering. Getting an LLM to rerank effectively depends heavily on carefully crafted prompts, which can be time-consuming and require specific expertise. Researchers are exploring how to automate this crucial step. One exciting new approach, called APEER (Automatic Prompt Engineering Enhances LLM Reranking), automatically refines prompts, boosting the performance of LLM reranking. APEER uses a clever two-step process: First, it gets feedback on an initial prompt, then uses this feedback to generate a better one. Second, it goes a step further, using a set of good and bad examples to fine-tune the prompt even more. The results are impressive. APEER significantly improves the quality of search results across several LLMs and different search tasks. What's even more exciting is that APEER's prompts seem to be transferable—prompts trained on one model often work well on others, and prompts trained for one type of search task often work on other search tasks as well. This adaptability could save AI engineers significant time and resources. APEER represents a notable step towards automating a key part of the LLM pipeline. While more research is needed, this approach holds immense potential for streamlining search and information retrieval in the age of LLMs. It's an example of how AI is not only changing how we find information, but also building better tools to build itself.