{
  "id": "automatic-prompt-engineer",
  "code": "PTL-0073",
  "term": "Automatic Prompt Engineer",
  "aliases": [
    "APE"
  ],
  "category": "optimization",
  "definition": "Automatic Prompt Engineer (APE) uses a language model to generate candidate instructions for a task from input-output examples, scores each candidate on held-out data, and selects the best one.",
  "description": "APE discovered a zero-shot chain-of-thought trigger that outperformed \"Let's think step by step\" on some benchmarks, and framed prompt writing as a search problem that models can solve themselves.",
  "example": null,
  "broader": [],
  "narrower": [],
  "related": [
    "prompt-engineering",
    "opro",
    "dspy",
    "meta-prompting"
  ],
  "introduced": 2022,
  "sources": [
    {
      "title": "Large Language Models Are Human-Level Prompt Engineers",
      "authors": "Zhou et al.",
      "year": 2022,
      "url": "https://arxiv.org/abs/2211.01910"
    }
  ],
  "url": "https://protologue.com/t/automatic-prompt-engineer/",
  "citation": "Protologue. (2026). Automatic Prompt Engineer. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.0.0, PTL-0073). https://protologue.com/t/automatic-prompt-engineer/"
}