{
  "dataset": "glossary",
  "record": {
    "id": "reranking",
    "term": "Reranking",
    "category": "knowledge-memory",
    "short_def": "A second retrieval stage that re-scores an initial set of candidate passages with a more precise model, promoting the most relevant to the top.",
    "long_def": "Reranking improves retrieval precision: a fast first stage (vector or keyword search) returns many candidates, then a slower, more accurate cross-encoder re-scores each against the query and reorders them. The agent then reads only the top few, so quality rises without a larger context budget.",
    "see_also": [
      "rag",
      "semantic-search",
      "chunking"
    ],
    "etymology_origin": {
      "value": null,
      "verify_status": "verify-against-primary-at-build",
      "source_hint": "https://en.wikipedia.org/wiki/Learning_to_rank — reranking builds on the learning-to-rank tradition in information retrieval"
    },
    "related_to": [
      "rag",
      "semantic-search",
      "chunking",
      "embeddings"
    ],
    "contrast_with": "Unlike first-stage vector search optimized for recall and speed, a reranker optimizes for precision — it is slower but far better at ordering the final few results.",
    "example": "A search returns 50 candidate passages; a reranker re-scores them and moves the 3 truly relevant ones to the top, which is all the agent reads.",
    "source": "https://en.wikipedia.org/wiki/Learning_to_rank",
    "status": "active",
    "why_it_matters": "Reranking is how an agent spends a small context budget on the highest-signal passages — the retrieval-side counterpart to writing content an engine can rank cleanly.",
    "sameAs": [
      "https://en.wikipedia.org/wiki/Learning_to_rank"
    ],
    "bridge_entity": "rag",
    "last_verified": "2026-07-06",
    "md_twin": "/glossary/reranking.md"
  }
}