Imagine scrolling through your favorite app and seeing ads perfectly tailored to your interests. That's the promise of Large Language Models (LLMs) like those used in cutting-edge recommendation systems. But how do they transform raw data into compelling ad experiences? A new research paper unveils a system called LEADRE, a groundbreaking framework designed to harness the power of LLMs for display advertising. Traditional systems often rely on simplistic identifiers, failing to grasp the nuances of user preferences and ad content. LEADRE takes a different approach. It delves into the rich world of user data, from demographics to browsing history, even factoring in their engagement with non-ad content like videos and news articles. This multi-faceted knowledge is then carefully crafted into prompts that guide the LLM in generating relevant ads. However, simply understanding user interests isn't enough. LEADRE goes a step further by aligning the LLM with the specific nuances of the advertising world. It uses auxiliary tasks to teach the model the relationships between ad features, categories, and those all-important semantic IDs that link textual descriptions to actual ads. And because businesses need results, LEADRE employs a technique called Direct Preference Optimization (DPO) to encourage the LLM to generate ads with high business value. Finally, to meet the demands of real-time advertising, LEADRE deploys a clever hybrid system that combines both latency-tolerant and latency-sensitive services. This allows the system to generate ad recommendations on the fly while keeping the computational costs manageable. Tests on WeChat Channels and Moments show LEADRE boosting Gross Merchandise Value (GMV) by 1.57% and 1.17% respectively. This research marks a significant step forward in personalized advertising, showcasing the potential of LLMs to deliver truly engaging and relevant ad experiences. While promising, challenges remain. The next step is to enhance LLMs to predict not just one, but a sequence of relevant ads, capturing an even richer understanding of user intent. Improving the way ads are represented within the system is also crucial. As LLMs continue to evolve, expect even more sophisticated and personalized ad recommendations in the future, blurring the line between advertising and genuinely useful content.