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Agentic Multi-Source Grounding for Enhanced Query Intent Understanding: A DoorDash Case Study
🤖AI Summary
DoorDash developed an AI system that uses multiple data sources to better understand ambiguous search queries by combining catalog data with web search results. The system achieved significant accuracy improvements over traditional methods and is now deployed across 95% of DoorDash's daily search traffic.
Key Takeaways
- →DoorDash's new AI system improves query understanding accuracy by 10.9pp over ungrounded LLMs and 4.6pp over legacy systems.
- →The system combines catalog entity retrieval with autonomous web search to resolve ambiguous queries like 'Wildflower'.
- →On long-tail queries, the system achieves 90.7% accuracy, representing a 13.0pp improvement over baseline.
- →The architecture is designed to be generalizable across different marketplace domains without core modifications.
- →The system is currently deployed in production serving over 95% of DoorDash's daily search impressions.
#ai#machine-learning#search#nlp#doordash#information-retrieval#marketplace#query-understanding#production-ai
Read Original →via arXiv – CS AI
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