Personalized storefronts in large e-commerce marketplaces are often assembled from many independent components: static themes per page section (''placement''), retrieval systems to fetch eligible products per placement, and pointwise rankers to order content. While effective in optimizing for aggregate preferences, this paradigm is rigid and can limit personalization and semantic cohesion across the page. This makes it poorly suited to support dynamic objectives and merchandising requirements over time. To address this, we introduce a cascaded merchandising framework that decomposes storefront construction into two generative tasks: (i) placement-level theme generation and (ii) constrained keyword generation per placement to power product retrieval. Teacher–student fine-tuning is leveraged to improve scalability of this framework under production latency and cost constraints. Fine-tuned model ablations are shown to approach closed-weight LLM performance. We further contribute frameworks for AI-driven content evaluation and quality filtering, enabling safe and automated deployment of dynamic content at scale. Generative output is fused with traditional ranking models to preserve hybrid infrastructure. In online experiments, this framework yields an estimated +2.7% lift in cart adds per page view over a strong baseline.

A number of researchers have recently questioned the necessity of increasingly complex neural network (NN) architectures.In particular, several recent papers have shown that simpler, properly tuned models are at least competitive across several NLP tasks.In this work, we show that this is also the case for text generation from structured and unstructured data.We consider neural tableto-text generation and neural question generation (NQG) tasks for text generation from structured and unstructured data, respectively.Table -to-text generation aims to generate a description based on a given table, and NQG is the task of generating a question from a given passage where the generated question can be answered by a certain sub-span of the passage using NN models.Experimental results demonstrate that a basic attentionbased seq2seq model trained with the exponential moving average technique achieves the state of the art in both tasks.Code is available at https://github.com/h-shahidi/ 2birds-gen.