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LLM-Powered Discovery Recommendations

1 post · Feb 2026

What this trail covers

Instacart is transforming its discovery recommendations by leveraging Large Language Models (LLMs). This initiative moves away from static content libraries and human-driven generation towards an AI-native platform for content generation, evaluation, and retrieval. The system employs a top-down, cascaded approach, starting with page design and theme generation personalized to users, followed by retrieval keyword generation, quality and diversity filtering, and finally, product and pagewise ranking.

All Instacart trails
  1. Our Early Journey to Transform Instacart’s Discovery Recommendations with LLMsThis post details Instacart's early journey in transforming discovery recommendations using LLMs. It outlines a top-down, cascaded generative content pipeline for the Shopping Hub, moving from static content libraries to an AI-native platform. The pipeline includes phases for page design & theme generation, retrieval keyword generation, quality & diversity filtering, and product & pagewise ranking. Key learnings emphasize the benefits of a cascaded approach over a single-step model for personalization, cohesion, and adaptability, and highlight the use of constrained decoding and retrieval-augmented generation (RAG) techniques.