Search and Recommendations Archives

Building Ask DoorDash (Part 5): A Grounded Interface For Shopping Agents

This post covers the client and platform boundaries behind user experience and interactions with Ask DoorDash: how we moved from hackathon carousels to that model, how one artifact serves three readers, and how context and state stay consistent as consumers move through DoorDash.

How DoorDash Built a Centralized Gateway for AI Agent-Tool Access

AI agents become useful when they can take action in real systems.

Building Ask DoorDash (Part 4): A Platform for Building and Evolving Agents

Introduction

We built Ask DoorDash on a common platform that lets domain teams build and evolve their agents without rebuilding the systems beneath them.

Building Food Metadata with LLM Juries, Context Optimization & Multimodal AI

DoorDash serves a vast and diverse set of merchants, with every restaurant, menu, and dish expressed in its own unique way.

Using small language models to serve more relevant DoorDash search ads

When consumers search on DoorDash, they are usually trying to accomplish something quickly.

Building a unified consumer memory for personalization at scale

DoorDash’s marketplace spans restaurants, groceries, convenience stores, retail outlets, and more.

Offline LLMs, Online Personalization: Generating carousels at DoorDash

Recommendation systems provide highly personalized results, but building hyperpersonalized experiences remains challenging because of the bottlenecks created by content generation and presentation.

Using LLMs to build content embeddings for search and recommendations

Header Image Description: Example of semantic meaning beyond engagements

A persistent bottleneck has constrained search and recommendation functions at DoorDash for years — the caliber of content embedding depends on data quality, while personalization depends on embedding quality.