Imagine searching for information, but instead of just keywords, you could specify different aspects or "facets" of what you're looking for. Want articles about AI in healthcare, but focused on ethical implications, not technical details? That's the power of faceted search. But current AI struggles with this nuanced approach. A new research paper introduces "Multi-Facet Blending" (FaBle), a clever technique to help AI grasp the complexities of these searches. FaBle uses the surprising power of modularity – breaking down documents into their core components (like background, method, and results in a research paper) and then recombining them in novel ways. This creates synthetic examples that train the AI to understand how different facets contribute to relevance. Tested on scientific papers and educational test questions, FaBle shows promising results, especially in areas where traditional AI methods fall short, like comparing research methods or finding similar test questions with different stories and options. This innovative approach opens doors to more intelligent search engines and recommendation systems that truly understand our multifaceted needs. Challenges remain, particularly in data scarcity and generating more nuanced "hard negative" examples to further refine the AI's understanding. But FaBle marks a significant step towards unlocking AI's ability to handle complex, faceted queries, making information discovery more precise and efficient.