{
  "id": "skeleton-of-thought",
  "code": "PTL-0035",
  "term": "Skeleton-of-Thought",
  "aliases": [],
  "category": "reasoning",
  "definition": "Skeleton-of-thought first asks the model for a brief outline of its answer, then expands each outline point in parallel, reducing end-to-end generation latency.",
  "description": "Because the points are expanded independently, it suits list-like answers better than tightly sequential reasoning.",
  "example": null,
  "broader": [],
  "narrower": [],
  "related": [
    "parallelization",
    "prompt-chaining"
  ],
  "introduced": 2023,
  "sources": [
    {
      "title": "Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generation",
      "authors": "Ning et al.",
      "year": 2023,
      "url": "https://arxiv.org/abs/2307.15337"
    }
  ],
  "url": "https://protologue.com/t/skeleton-of-thought/",
  "citation": "Protologue. (2026). Skeleton-of-Thought. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.0.0, PTL-0035). https://protologue.com/t/skeleton-of-thought/"
}