Instead of trusting embedding similarity alone, this pulls a wider pool of candidate passages, then a cross-encoder reranker scores each one against the question directly — a slower but more accurate second pass. The top three reranked passages each generate their own answer, so you can compare which passage actually answered the question best.
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