{
  "id": "topic:re-ranking",
  "_kind": "Topic",
  "_file": "domain/topics/topics-knowledge-patterns.yaml",
  "_cluster": "domain",
  "attributes": {
    "displayName": "Re-Ranking",
    "description": "Re-Ranking as a cross-cutting topic — applying cross-encoder models\nto re-score retrieved documents for relevance after initial retrieval.\nCovers the bi-encoder (fast, approximate) vs cross-encoder (slow,\naccurate) trade-off, popular re-ranker models (Cohere Rerank, BGE\nReranker, cross-encoder/ms-marco), ColBERT-style late interaction\nfor efficient re-ranking, and the retrieve-then-rerank pipeline\npattern where a cheap first-stage retriever fetches candidates and\nan expensive re-ranker selects the best.\n"
  },
  "outgoingEdges": [
    {
      "from": "topic:re-ranking",
      "to": "domain:software-engineering",
      "kind": "applies_to"
    },
    {
      "from": "topic:re-ranking",
      "to": "domain:data-science",
      "kind": "applies_to"
    },
    {
      "from": "topic:re-ranking",
      "to": "specialization:ai-agents-conversational",
      "kind": "applies_to"
    }
  ],
  "incomingEdges": [
    {
      "from": "domain:knowledge-management",
      "to": "topic:re-ranking",
      "kind": "contains"
    },
    {
      "from": "topic:rag-pipeline-design",
      "to": "topic:re-ranking",
      "kind": "related_topics"
    }
  ]
}