Prompt Generation: A Configuration-Driven Framework for Decoupling Feature Processing in Generative Retrieval

Jul 14, 2026

Researchers introduce Prompt Generation (PG), a configuration-driven framework that decouples feature-processing logic from model architecture using declarative JSON files. This approach standardizes feature processing, enabling faster training iteration, streamlined deployment, and efficient online inference. Deployed on Taobao Search, PG achieved statistically significant uplifts of +0.47% in transaction count and +0.51% in gross merchandise value (GMV) during online A/B tests.

Why it matters: PG demonstrates a practical advance in industrial AI systems by reducing engineering complexity and accelerating deployment for large-scale search and recommendation platforms.

Full story at: arXiv Information Retrieval ↗