Using LLMs to amplify human labeling and improve Dash search relevance

In RAG, the ranker decides which handful of documents the model ever sees. This is a worked approach to scaling relevance labels with LLMs when human judgments are too scarce to train on.

• RAG — Retrieval-augmented generation — fetching relevant documents first and pasting them into the model's context so it answers from your data instead of memory.

• videoDistill the LLM, Don't Serve It: Search & Personalization at DoorDash — Raghav Saboo, DoorDashAI Engineer
• articleHow we optimized Dash's relevance judge with DSPyIlya Yakovlev,Andrew Cheung,Binoy Dash,Simran Jumani,Dmitriy Meyerzon,Mark Breitenbach,Ishan Mishra,Kazuaki Okumura,Mike White,Kevin Altschuler,Facundo Agriel,Ishan Mishra,Eric Wang,Dmitriy Meyerzon
• articleHow we used DSPy to turn AI evaluations into better responses in Dash chatIlya Yakovlev,Andrew Cheung,Binoy Dash,Simran Jumani,Dmitriy Meyerzon

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