Overview
Direct Answer
Reranking is a two-stage retrieval architecture where a computationally efficient initial retriever generates candidate documents, which are then rescored by a more sophisticated model to refine relevance ordering. This approach balances computational cost against ranking accuracy by applying expensive models only to a pruned candidate set.
How It Works
The first stage employs a lightweight retriever—typically lexical search or a fast neural encoder—to retrieve the top-k candidates from a large corpus. The second stage applies a cross-encoder or more complex neural model to directly score each candidate pair against the query, producing refined relevance scores that reorder the initial results. The final ranked list reflects both stages' contributions.
Why It Matters
Organisations require high-quality ranking for search relevance, personalisation, and recommendation systems, but applying expensive models to millions of documents is computationally prohibitive. Reranking reduces end-to-end latency and infrastructure costs whilst achieving near-maximum accuracy, making it essential for real-time production systems handling high query volume.
Common Applications
Reranking is deployed in e-commerce search (product ranking by relevance and purchase probability), legal discovery (document prioritisation by case relevance), and question-answering systems (selecting candidate passages before answer generation). Information retrieval pipelines in academic search, job boards, and content recommendation platforms similarly rely on multi-stage ranking.
Key Considerations
The quality ceiling depends on initial retrieval quality; a poor first stage cannot be fully remedied by reranking. Trade-offs exist between latency (deeper candidate sets improve accuracy but increase reranking cost) and ranking accuracy, requiring careful tuning per use case.
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