DoorDash improves search retrieval using LLMs and RAG, increasing dish carousel trigger rate by ~30%
DoorDash's search system had to handle complex multi-requirement queries while enforcing hard retrieval rules such as dietary restrictions, but traditional query segmentation methods (PMI, n-gram analysis) fell short on complex or ambiguous queries, and embedding-based retrieval systems returned imprecise candidate sets that ignored hard constraints.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · User submits search query
Users commonly conduct searches using precise queries that compound multiple requirements.
Tools used
LLMsRAGANNBM25knowledge graph
Outcome
After deploying LLM-based query understanding with RAG entity linking, DoorDash achieved nearly a 30% increase in dish carousel trigger rate, more than a 2% increase in whole page relevance for dish-intent queries, and a further 1.6% WPR improvement from a retrained ranker, with same-day conversions also rising.
What failed first
Traditional statistical query segmentation methods (PMI and n-gram analysis) could not capture contextual relationships in complex queries, and standalone embedding-based retrieval systems returned imprecise results that ignored hard constraints such as dietary preferences.