Reranking
Reranking is a second, slower pass over search results: a cross-encoder model reads each candidate together with the query and rescores it for true relevance, so the top results are the actually-right ones, not just the roughly-similar ones.
No jargon, no math
In plain words, what is it?
Vector search is fast because it compresses the query and the documents separately: fast, but shallow. Reranking adds a second read. First pass: an embedding search pulls the top 50 candidates in a few milliseconds, tuned for recall. Second pass: a cross-encoder reads the query and each candidate together, pair by pair, and assigns a precise relevance score; the list is reordered and only the top 5 reach the model that writes the answer. The cost: joint reading is slow, which is why you reserve it for a shortlist. The limit: if the right document was not in the first-pass 50, reranking never sees it, so the first pass is tuned to avoid losing it.
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First pass. A fast embedding search pulls the top 50 candidates.
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Joint reading. The cross-encoder reads the query and each candidate together, pair by pair.
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Rescoring. Each pair gets a precise relevance score and the list is reordered.
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Hand-off. Only the top 5 reach the model, quick to read and cite.
Terms that live nearby.
Semantic Search
Semantic search finds content by meaning instead of exact words: the query and the documents are converted into embeddings, and the closest matches are returned even when no keyword overlaps.
ReadEmbedding
An embedding is a list of numbers that captures the meaning of a piece of text, image or audio, so that content with similar meaning gets similar numbers and the computer can measure closeness with plain arithmetic.
ReadVector Database
A vector database is a database built to store numeric fingerprints of content (embeddings) and find the closest ones to any query in milliseconds, powering semantic search, recommendations and AI retrieval.
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