{
  "id": "zero-shot-prompting",
  "code": "PTL-0008",
  "term": "Zero-shot Prompting",
  "aliases": [],
  "category": "foundations",
  "definition": "Zero-shot prompting asks a model to perform a task from an instruction alone, without any worked examples in the prompt.",
  "description": "Zero-shot performance improved dramatically with instruction tuning and preference training, which taught models to follow natural-language task descriptions. It is the default for most modern chat use, with examples added only when the format or judgment required is hard to describe.",
  "example": null,
  "broader": [],
  "narrower": [],
  "related": [
    "few-shot-prompting",
    "instruction-tuning",
    "zero-shot-chain-of-thought"
  ],
  "introduced": null,
  "sources": [
    {
      "title": "Language Models are Few-Shot Learners",
      "authors": "Brown et al.",
      "year": 2020,
      "url": "https://arxiv.org/abs/2005.14165"
    },
    {
      "title": "Finetuned Language Models Are Zero-Shot Learners",
      "authors": "Wei et al.",
      "year": 2021,
      "url": "https://arxiv.org/abs/2109.01652"
    }
  ],
  "url": "https://protologue.com/t/zero-shot-prompting/",
  "citation": "Protologue. (2026). Zero-shot Prompting. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.0.0, PTL-0008). https://protologue.com/t/zero-shot-prompting/"
}