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Retrieval-Augmented Generation works by pulling relevant documents from a knowledge base and feeding them to a language model. The model reads that context, generates an answer — and that's where it stops. There's no explanation of which part of the retrieved context actually drove the response. Wa…

Retrieval-Augmented Generation works by pulling relevant documents from a knowledge base and feeding them to a language model. The model reads that context, generates an answer — and that's where it stops. There's no explanation of which part of the retrieved context actually drove the response. Was it the first sentence? The last paragraph? Something buried in the middle? That lack of transparency is a real problem, especially when the answer is wrong.