{
  "dataset": "glossary",
  "record": {
    "id": "fine-tuning",
    "term": "Fine-Tuning",
    "category": "knowledge-memory",
    "short_def": "Continuing a pretrained model's training on a smaller, task-specific dataset to specialize its behavior for a particular domain or format.",
    "long_def": "Fine-tuning adapts an already-trained model by updating its weights on curated examples, so it learns a style, a domain vocabulary or a structured-output format the base model does not reliably produce. It is a form of transfer learning, distinct from retrieval, which adds knowledge at inference time without changing weights.",
    "see_also": [
      "rlhf",
      "rag",
      "embeddings"
    ],
    "etymology_origin": {
      "value": null,
      "verify_status": "verify-against-primary-at-build",
      "source_hint": "https://en.wikipedia.org/wiki/Fine-tuning_(deep_learning) — established transfer-learning term in deep learning"
    },
    "related_to": [
      "rlhf",
      "rag",
      "embeddings",
      "transformer"
    ],
    "contrast_with": "Unlike retrieval-augmented generation, which supplies knowledge at query time and changes no weights, fine-tuning bakes behavior into the model by training on examples.",
    "example": "A team might fine-tune a model on their support transcripts so it adopts their tone and product vocabulary without needing those examples in every prompt.",
    "source": "https://en.wikipedia.org/wiki/Fine-tuning_(deep_learning)",
    "status": "active",
    "why_it_matters": "Fine-tuning and retrieval are the two levers for specializing an agent; the agentic web leans on retrieval, since machine-readable content updates a model's knowledge without retraining it.",
    "sameAs": [
      "https://en.wikipedia.org/wiki/Fine-tuning_(deep_learning)"
    ],
    "bridge_entity": "rag",
    "last_verified": "2026-07-06",
    "md_twin": "/glossary/fine-tuning.md"
  }
}