{
  "id": "bbg-p0326-ai-models-and-llm-systems",
  "title": "Full Fine-Tuning vs LoRA vs RAG",
  "chapter": "ai-models-and-llm-systems",
  "batch": "17",
  "rank": 164,
  "sourcePage": 326,
  "sourcePointer": "p. 326",
  "status": "accepted",
  "reviewerStatus": "reviewed",
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  "fireworksTechGraph": {
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    "diagramType": "comparison",
    "topologyNotes": [
      "source page render inspected",
      "extracted page text inspected",
      "source page render and extracted text inspected",
      "preserve adaptation comparison: full weight updates, low-rank adapters, and runtime retrieval",
      "omit decorative source panels"
    ],
    "publicBoundary": [
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  "callouts": [],
  "sourceReview": {
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      "concept: full fine-tuning updates all weights",
      "concept: LoRA changes small adapters",
      "concept: RAG retrieves external knowledge",
      "concept: compute and storage tradeoffs"
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    "labelSource": "curated",
    "semanticStatus": "reviewed"
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  "groups": [
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