Imagine a world where AI can instantly determine if a product truly matches a shopper's search, eliminating endless scrolling and irrelevant results. That's the promise of new research from Walmart, exploring how Large Language Models (LLMs) can automate product relevance judgment. Researchers tackled the challenge of training LLMs to understand the nuances of product search, using a massive dataset of millions of query-item pairs meticulously labeled by humans. They experimented with different techniques, including fine-tuning billion-parameter LLMs and optimizing how product information is fed to the models. The results? LLMs demonstrated a remarkable ability to judge relevance, achieving accuracy comparable to human evaluators. In simulated feature launch experiments, the AI's judgment aligned with human decisions up to 89% of the time, suggesting LLMs could revolutionize how search relevance is evaluated. This breakthrough could lead to more efficient and scalable product search, getting shoppers what they want faster. However, challenges remain, such as handling ambiguous queries and numerical ranges. Future research will explore advanced techniques like self-distillation and synthetic data generation to further enhance LLM performance in product search.