Never Miss an Episode: How LLMs are Powering Serial Content Discovery on YouTube

Leveraging large language models (LLMs) through prompting presents a cost-effective approach to build scalable systems without traditional model training. This paper showcases the effectiveness of using simple few-shot LLM prompt to develop a scalable and easily maintainable system that addresses a real-world user need. A critical user journey in video recommendation is watching serial content, which requires viewing episodes in a specific sequence. The existing method on YouTube for identifying serial content relied on manual creator tagging of playlists or inflexible regular expressions. These methods proved difficult to maintain and scale, limiting the system's ability to effectively identify and recommend serial content. This paper demonstrates that a carefully designed few-shot LLM prompt can accurately identify serial playlists at scale, improving user experience with minimal engineering. The paper details the challenges and lessons learned in developing and deploying this prompting-based system.