The answer
Sparse retrieval matches exact words (like TF-IDF). Dense retrieval matches vector meanings (embeddings).
Sparse retrieval matches exact words (like TF-IDF). Dense retrieval matches vector meanings (embeddings).
Running a high-quality model on the top retrieved results to re-order them based on relevance before sending them to the LLM.
Because it combines the conceptual understanding of embeddings with the precision of keyword matching for codes and terms.
It requires maintaining both a keyword index and a vector index, which can increase storage and indexing times.
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