What is reranking and why does it matter?

Reranking is the process of fine-tuning search results using powerful neural models to improve relevance, precision, and recall. It involves applying the best characteristics of different models at an appropriate stage in the query pipeline to get fast and relevant results. High recall is crucial for ensuring potentially relevant documents show up in the result set, while high precision ensures the best result appears above the fold. Neural search platforms like Vectara have built-in understanding of human language, achieving high recall without manual configuration, but slower reranking models can be used to fine-tune results and achieve precise scores.

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