• 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.

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.

• 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
• articleImproving Recommendation Systems & Search in the Age of LLMseugeneyan.com

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