Imagine searching for "best Italian restaurants near me" and getting results for shoe stores. That's a relevance problem. Commercial search engines constantly grapple with accurately connecting your search (the query) to the most relevant product, service, or piece of information (the item). Large Language Models (LLMs) are increasingly used for this task, but they face challenges. A new research paper introduces CPRM, a continual pre-training framework designed to boost LLMs' relevance modeling skills. Existing LLMs are often trained on general data, lacking the specific nuances of product descriptions, service details, or user reviews. CPRM tackles this by continually training the LLM on search-specific data, including queries paired with multi-field items like titles, keywords, categories, and descriptions. This helps the LLM bridge the 'semantic gap' between short, colloquial queries and more formal, detailed item descriptions. CPRM doesn't stop there. It leverages the power of in-context learning by pre-training the LLM on sequences of related queries or items, mirroring real-world search patterns. Imagine the LLM learning that searches for "Italian food," "pasta dishes," and "pizza near me" are all related and often lead to clicks on similar restaurants. Finally, CPRM goes a step further by using reading comprehension techniques. Think of it as the LLM actively summarizing and paraphrasing product descriptions, generating related background information, and even inventing diverse queries related to the item. This deep dive into the data helps the LLM generate more relevant search results. Tests on real-world industry data show CPRM significantly outperforms existing baselines, improving relevance scores and reducing errors in filtering search results. In online A/B tests, CPRM increased the click-through rate on relevant results. This research shows promising steps towards smarter, more relevant search experiences, helping us find exactly what we're looking for amidst the vast ocean of online information.