E-commerce platforms in emerging markets often operate with underdeveloped product catalogs that contain only category taxonomies but lack structured attribute schemas. This absence of fine-grained product attributes limits search capabilities---preventing faceted filtering, degrading query understanding, and weakening semantic representations used by search systems. We present BEATS, a human-in-the-loop LLM framework for bootstrapping product attribute taxonomies entirely from scratch. Our approach extends a multi-stage LLM generation pipeline with two critical production stages: (1) proactive quality checking by model developers to filter erroneous outputs, and (2) human annotation by domain-expert local staff to validate generated attributes. The framework operates iteratively---prompts at each generation stage are refined based on quality check observations and annotator feedback across successive rounds, progressively improving attribute quality. Once the attribute taxonomy is established, we employ LLMs to perform structured attribute tagging on individual product items, enriching their contextual representations. The enriched catalog directly benefits multiple components of the search system: enabling granular attribute-based filtering, providing structured features for ranking models, and improving semantic representations for dense retrieval. We validate the generated taxonomy by training dense retrieval models on attribute-enriched product data, demonstrating consistent improvements over baselines using original catalog information. Our system has been deployed at Rakuten Taiwan, enriching 9 major categories spanning 2,694 sub-categories with 67,277 generated attributes, and over 5.4 million products have been tagged with the generated attributes, with plans to enrich the entire product catalog.

Retrieval-augmented generation (RAG) has long been used to guide generative models in producing more accurate answers, with most discussions focusing on reading comprehension-based question answering (QA). However, their role in real-world answer rehearsal scenarios remains underexplored. The rise of large language models (LLMs) presents new opportunities to develop systems that assist professionals, making this discussion both timely and essential. This paper explores how to better support corporate executives in answering questions from professional analysts during earnings calls. We compare the impact of two external knowledge sources: large-scale causal knowledge graphs (KGs) and historical Q&A records-retrieved either from a global pool or company-specific archives. Our findings suggest that a company's historical Q&A records are more influential than causal KGs in improving response quality. To the best of our knowledge, this is the first study to systematically compare and analyze different knowledge resources in answer rehearsal. Our findings show the potential of inspiring future research on the interplay between KG and historical QA pairs for answer rehearsal.