What is Reranking?
Turkish: Yeniden Sıralama (Reranking)
Reranking re-scores an initial result set with a stronger model so the most relevant documents move to the top reliably.
What is Reranking?
Reranking is a second evaluation step in search systems. The first search stage is fast and broad; the reranker reads a smaller set of candidates with the query and gives a more precise relevance score.
For example, semantic search may retrieve 50 support articles. Reranking checks how well each article truly matches the user’s question and moves the best 5 results to the top.
How It Works
The first stage may use embedding similarity, keyword search, or hybrid search. The second stage uses a model similar to a cross-encoder to evaluate the query and document passages together. This can be slower, so it usually runs on candidate results rather than the full dataset.
Reranking does more than change list order. In RAG systems, it decides which sources are sent to the model, so it affects answer quality.
Business Use
Reranking reduces misleading but similar-looking results in internal knowledge search, e-commerce product search, help centers, and contract archives. It is especially useful for short queries, synonyms, and terms with multiple meanings.
A good implementation measures latency, cost, and accuracy together. Instead of running expensive reranking for every query, teams often apply it to critical searches or after a specific candidate count.
Related Terms
An embedding represents text, images, products, or other data as numeric vectors that can be compared for semantic similarity.
RAG (Retrieval-Augmented Generation)RAG is an AI architecture where a language model retrieves relevant passages from documents or databases before generating an answer.
Semantic SearchSemantic search finds relevant results by comparing the meaning of queries and content, not only matching exact keywords.