Skeleton-of-Thought
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.
Sources
- Ning et al. (2023). Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generation.
Cite this entry
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/
BibTeX
@misc{protologue_skeleton_of_thought,
title = {Skeleton-of-Thought},
author = {{Protologue}},
year = {2026},
howpublished = {Protologue: A Taxonomy of Prompting and LLM Techniques, v1.0.0},
note = {Entry PTL-0035},
url = {https://protologue.com/t/skeleton-of-thought/}
}