{
  "id": "active-prompting",
  "code": "PTL-0023",
  "term": "Active Prompting",
  "aliases": [
    "Active-Prompt"
  ],
  "category": "exemplars",
  "definition": "Active prompting selects which questions to annotate with chain-of-thought exemplars by choosing those on which the model is most uncertain, measured by disagreement across sampled answers.",
  "description": "Borrowing from active learning, it focuses human annotation effort on the examples most informative for the model, rather than on a fixed or random set.",
  "example": null,
  "broader": [
    "exemplar-selection"
  ],
  "narrower": [],
  "related": [
    "chain-of-thought",
    "self-consistency"
  ],
  "introduced": 2023,
  "sources": [
    {
      "title": "Active Prompting with Chain-of-Thought for Large Language Models",
      "authors": "Diao et al.",
      "year": 2023,
      "url": "https://arxiv.org/abs/2302.12246"
    }
  ],
  "url": "https://protologue.com/t/active-prompting/",
  "citation": "Protologue. (2026). Active Prompting. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.0.0, PTL-0023). https://protologue.com/t/active-prompting/"
}