Knowledge distillation

Sep 25, 2026

Distill the LLM, Don't Serve It: Search & Personalization at DoorDash — Raghav Saboo, DoorDash

Raghav Saboo, Staff ML Engineer at DoorDash, argues that semantic understanding, not engagement, is the real bottleneck in marketplace discovery. He walks through four LLM-driven primitives (graded relevance supervision, semantic IDs, multi-timescale consumer memory, and steerable personalized collections) and explains the core pattern of reasoning offline with LLMs and distilling that into fast serving models.

Aug 14, 2025

Traditional vs LLM Recommender Systems: Are They Worth It?

This summary explores Arpita Vats's insights on how Large Language Models (LLMs) are revolutionizing recommender systems. It contrasts the traditional feature-engineering-heavy approach with the contextual understanding of LLMs, which shifts the focus to prompt engineering. Key challenges like inference latency and cost are discussed, along with practical solutions such as lightweight models, knowledge distillation, and hybrid architectures. The conversation also touches on advanced applications like sequential recommendation and the future potential of agentic AI.