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RAGHard· Training Data· Silver Labeling· Retrieval Systems
How would you get training labels for a source router w...
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See plansManual annotation or LLM-as-judge passes are inefficient for scaling. Instead, leverage the existing retrieval system by running the expensive broadcast once in a batch offline process. By taking the global top-k merged results, you can automatically label a source as positive if it contributed at least one item to the final result set. This provides a free, high-quality signal that is affordable in batch at training time, even if it would be too expensive to run online at serving time.