This paper explores the integration of Large Language Models (LLMs) with search engine services, focusing on two main areas: using search engines to improve LLMs (Search4LLM) and enhancing search engines using LLMs (LLM4Search). The integration of LLMs and search engines offers significant potential to enhance search functionalities and redefine user interaction with digital information systems. Search4LLM examines how search engines can provide diverse, high-quality datasets for LLM pre-training, help LLMs learn to answer queries more accurately, and improve their precision through Learning-to-Rank (LTR) tasks. LLM4Search explores how LLMs can summarize content for better indexing, improve query outcomes through optimization, and enhance search result ranking by analyzing document relevance. Challenges include addressing biases, ethical issues, and the computational costs of integrating LLMs into search services. The paper also discusses broader implications for service computing, such as scalability, privacy, and adapting search engine architectures for advanced models. Key contributions include exploring the use of search engine data for LLM pre-training, leveraging high-quality ranked documents for training, and advancing LTR technologies to enhance search results. The integration of LLMs and search engines presents a paradigm shift towards creating more intelligent, efficient, and user-centric search services. The paper outlines the technical and practical aspects of this integration, highlighting the potential benefits and challenges in advancing search technologies.This paper explores the integration of Large Language Models (LLMs) with search engine services, focusing on two main areas: using search engines to improve LLMs (Search4LLM) and enhancing search engines using LLMs (LLM4Search). The integration of LLMs and search engines offers significant potential to enhance search functionalities and redefine user interaction with digital information systems. Search4LLM examines how search engines can provide diverse, high-quality datasets for LLM pre-training, help LLMs learn to answer queries more accurately, and improve their precision through Learning-to-Rank (LTR) tasks. LLM4Search explores how LLMs can summarize content for better indexing, improve query outcomes through optimization, and enhance search result ranking by analyzing document relevance. Challenges include addressing biases, ethical issues, and the computational costs of integrating LLMs into search services. The paper also discusses broader implications for service computing, such as scalability, privacy, and adapting search engine architectures for advanced models. Key contributions include exploring the use of search engine data for LLM pre-training, leveraging high-quality ranked documents for training, and advancing LTR technologies to enhance search results. The integration of LLMs and search engines presents a paradigm shift towards creating more intelligent, efficient, and user-centric search services. The paper outlines the technical and practical aspects of this integration, highlighting the potential benefits and challenges in advancing search technologies.