The authors redesigned a large e-commerce storefront using a cascaded generative approach: first, an LLM generates themes for each page section ("placement"), then generates constrained keywords per placement to retrieve products. They use teacher-student fine-tuning to make this scalable under production latency and cost constraints, with fine-tuned models approaching closed-weight LLM performance. AI-driven content evaluation and quality filtering enable safe automated deployment. The generative output is fused with traditional ranking models. Online experiments show +2.7% lift in cart adds per page view over the baseline.
Main takeaways:
- Traditional e-commerce storefronts are rigid: static themes, independent retrieval, pointwise rankers—limits personalization and semantic cohesion.
- Cascaded generative approach: LLM generates placement themes, then generates keywords per placement to power product retrieval.
- Teacher-student fine-tuning makes this scalable and fast enough for production; fine-tuned models nearly match closed-weight LLM performance.
- AI-driven evaluation and quality filtering enable safe automated deployment of dynamic content at scale.
- Generative output fused with traditional rankers to preserve existing infrastructure; online A/B test shows +2.7% cart-add lift.