A fast first-stage search gathers candidates. A reranking model then reads each query-document pair and assigns a more precise relevance score.
Applying the model to every document would be expensive, so reranking uses a limited candidate set. It cannot recover a relevant document missed by the first stage.
When to use
Use it when top result ordering materially affects answer quality.