RAG_Reranking

Re-ranking is the simplest. Idea is pretty simple.

1. You assume that Embedding + Search algo are not 100% precise so you use Recall to your advantage and get similar high N (say 25) number of related chunks from corpus.

2. Second step is to use a powerful model to increase the Precision. You re-rank above N queries again so that you can change the relative ordering and now select Top K queries (say 3) to pass as a context where K < N thus increasing the Precision.

Re-rank Search Results uses Reranker.

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