{
  "dataset": "glossary",
  "record": {
    "id": "transformer",
    "term": "Transformer",
    "category": "knowledge-memory",
    "short_def": "The neural-network architecture, built on self-attention, that underlies virtually all modern large language models.",
    "long_def": "The transformer processes a sequence in parallel using self-attention, letting every token weigh its relationship to every other token. Introduced in 2017, it replaced recurrent architectures and made today's large language models — and the agents built on them — possible.",
    "see_also": [
      "context-window",
      "fine-tuning",
      "embeddings"
    ],
    "etymology_origin": {
      "value": null,
      "verify_status": "verify-against-primary-at-build",
      "source_hint": "https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture) — introduced in 'Attention Is All You Need', Vaswani et al., Google, 2017"
    },
    "related_to": [
      "context-window",
      "fine-tuning",
      "embeddings",
      "tokenization"
    ],
    "contrast_with": "Unlike earlier recurrent networks that read tokens one at a time, the transformer attends to the whole sequence in parallel, which is what enabled scaling to large context windows.",
    "example": "Every frontier model in the Model Matrix — Claude, GPT, Gemini — is a transformer at its core.",
    "source": "https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture)",
    "status": "active",
    "why_it_matters": "The transformer is the engine under every agent on the agentic web; its self-attention is why context — and how cheaply your content fits in it — matters so much.",
    "sameAs": [
      "https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture)"
    ],
    "bridge_entity": "context-window",
    "last_verified": "2026-07-06",
    "md_twin": "/glossary/transformer.md"
  }
}