{
  "id": "dense-retrieval",
  "code": "PTL-0126",
  "term": "Dense Retrieval",
  "aliases": [
    "semantic search",
    "embedding-based retrieval",
    "DPR",
    "bi-encoder retrieval"
  ],
  "category": "retrieval-tools",
  "definition": "Dense retrieval finds relevant passages by embedding the query and every passage as dense vectors, usually with a dual encoder, and returning the passages whose vectors are most similar to the query's.",
  "description": "Because it matches on meaning rather than shared words, dense retrieval finds paraphrases and related concepts that keyword search misses. It can miss exact identifiers, rare names, and codes, which is why production systems often combine it with BM25 in hybrid search and refine results with a reranker.",
  "example": null,
  "broader": [],
  "narrower": [],
  "related": [
    "embedding",
    "bm25",
    "hybrid-search",
    "reranking",
    "vector-database"
  ],
  "introduced": 2020,
  "added": "2026-10-10",
  "sources": [
    {
      "title": "Dense Passage Retrieval for Open-Domain Question Answering",
      "authors": "Karpukhin et al.",
      "year": 2020,
      "url": "https://arxiv.org/abs/2004.04906"
    }
  ],
  "url": "https://protologue.com/t/dense-retrieval/",
  "citation": "Protologue. (2026). Dense Retrieval. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.4.0, PTL-0126). https://protologue.com/t/dense-retrieval/"
}