{
  "id": "foundation-model",
  "code": "PTL-0103",
  "term": "Foundation Model",
  "aliases": [
    "frontier model",
    "general-purpose model"
  ],
  "category": "foundations",
  "definition": "A foundation model is a model trained on broad data at scale, usually with self-supervision, that can be adapted to a wide range of downstream tasks by prompting or fine-tuning.",
  "description": "The term was introduced in 2021 by researchers at Stanford to describe models such as BERT, GPT-3, and CLIP that serve as a common base for many applications. Large language models are the most prominent kind, but the term also covers image, audio, and multimodal models. Because many systems are built on the same few foundation models, their strengths and flaws are inherited widely.",
  "example": null,
  "broader": [],
  "narrower": [
    "large-language-model"
  ],
  "related": [
    "base-model",
    "fine-tuning"
  ],
  "introduced": 2021,
  "added": "2026-10-10",
  "sources": [
    {
      "title": "On the Opportunities and Risks of Foundation Models",
      "authors": "Bommasani et al.",
      "year": 2021,
      "url": "https://arxiv.org/abs/2108.07258"
    }
  ],
  "url": "https://protologue.com/t/foundation-model/",
  "citation": "Protologue. (2026). Foundation Model. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.4.0, PTL-0103). https://protologue.com/t/foundation-model/"
}