What is Reranking?
Reranking is the step of reordering an initial set of retrieved results or candidates using a more accurate but often slower model to improve relevance.
In data and retrieval pipelines, a fast first-stage retriever quickly returns a large pool of candidates. A second-stage reranker then scores and reorders only the top candidates with richer features or a heavier model.
This two-stage design balances speed and quality: the retriever handles scale while the reranker focuses compute on promising items, often using cross-encoders, gradient-boosted trees, or learned ranking models.
Reranking can incorporate user context, freshness signals, or business rules that were too expensive to apply during the initial retrieval.
Example
A search engine first uses BM25 to fetch the top 1,000 documents for a query, then applies a neural reranker to promote the 10 most relevant ones to the top of the result page.
Why it matters
Modern AI systems rely on reranking to deliver higher-quality search and recommendation results at interactive speeds, directly improving user satisfaction and engagement.
Frequently asked questions
Initial ranking (or retrieval) quickly finds many candidates; reranking reorders a smaller subset with a more sophisticated model for better precision.
Related terms
An embedding (or vector embedding) is a way to represent words, sentences, or other data as dense numerical vectors in a high-dimensional space so that similar items end up close together.
Semantic search retrieves information by understanding the meaning and intent of a query rather than relying on exact keyword matches.
Batch size is the number of training examples processed together in a single forward and backward pass during model training.
Chunking is the process of breaking large datasets, documents, or files into smaller, fixed-size or semantically meaningful segments. It is a common data preprocessing step in AI/ML pipelines to manage memory and enable efficient processing.
Cosine similarity measures how similar two vectors are by computing the cosine of the angle between them, ignoring their magnitudes.
Data augmentation is a technique that artificially increases the size and diversity of a training dataset by creating modified versions of existing data samples.