{
  "$type": "site.standard.document",
  "contributors": [
    {
      "did": "did:plc:igunvse2uemkwmci3igoxhu5",
      "displayName": "Oz Akan",
      "role": "author"
    }
  ],
  "coverImage": {
    "$type": "blob",
    "ref": {
      "$link": "bafkreid6t6rpwc3k7p4rludnhvgj3cd575fks67emfvmtjiq6aeo22esfm"
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  "description": "At the heart of every effective RAG implementation lies a crucial decision: which embedding model to use.",
  "path": "/techs/05-embedding-selection",
  "publishedAt": "2025-05-11T21:00:00.000Z",
  "site": "at://did:plc:igunvse2uemkwmci3igoxhu5/site.standard.publication/luminary-blog",
  "tags": [
    "aiml",
    "research",
    "embeddings"
  ],
  "textContent": "Retrieval-Augmented Generation (RAG) has emerged as a critical approach for extending large language models beyond their training data. At the heart of every effective RAG implementation lies a important decision: which embedding model to use. This choice will impact your system's performance, costs, and scalability.",
  "title": "Embedding Selection for RAG Systems"
}