---
source: https://iinventedlight.com/library/notes/2310-06117-step-back-prompting.md
title: "Take a Step Back: Step-Back Prompting"
license: CC-BY-4.0
license_url: https://creativecommons.org/licenses/by/4.0/
source_license: "CC BY 4.0; restated in our own words"
attribution: "Summary by iinventedlight (AI-assisted). Sources: arXiv, Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models, CC BY 4.0."
author: "iinventedlight (synthesis of cited sources); text drafted with AI assistance"
date: 2026-11-04
status: published
checked_on: 2026-11-05
as_of: 2026-11-05
confidence: medium
untested: "Results were not reproduced; only the arXiv abstract page was read."
category: ai-research
topic_area: "ai-research: prompting for reasoning"
tags: llm, prompting, reasoning, abstraction, arxiv
sources:
  - id: S1
    title: "Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models"
    publisher: arXiv
    url: https://arxiv.org/abs/2310.06117
    accessed: 2026-11-05
    license: "CC BY 4.0"
---
# Take a Step Back: Step-Back Prompting

## Short answer
Step-back prompting first asks the model a more general question: what concept or principle applies here? Only then does it answer the specific one. Grounding the reasoning in that abstraction improved accuracy on science, knowledge and multi-hop questions. [S1]

## How to use it
1. Given a detailed question, prompt the model to state the high-level concept or first principle behind it (the step-back question). [S1]
2. Have the model answer that general question.
3. Feed the principle back in and ask the original question, reasoning from it. [S1]

## Reported results
- Tested on PaLM-2L, GPT-4 and Llama2-70B. [S1]
- PaLM-2L gained 7% on MMLU Physics, 11% on MMLU Chemistry, 27% on TimeQA and 7% on MuSiQue. [S1]

## When to try it
Use it on questions buried in specifics, where the right path depends on recognizing the underlying rule. Compare it with chain-of-thought on your own tasks before adopting it.

## Claims
- id: C1
  claim: Step-Back Prompting has LLMs derive high-level concepts and first principles from detailed instances.
  source: S1
  evidence: "derive high-level concepts and first principles from instances"
- id: C2
  claim: Experiments used PaLM-2L, GPT-4 and Llama2-70B.
  source: S1
  evidence: "PaLM-2L, GPT-4 and Llama2-70B models"
- id: C3
  claim: Step-Back Prompting improved PaLM-2L on TimeQA by 27%.
  source: S1
  evidence: "TimeQA by 27%"

## Sources
- [S1] Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models. arXiv. https://arxiv.org/abs/2310.06117 . License: CC BY 4.0. Accessed 2026-11-05.

License: this note is CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Summary by iinventedlight (AI-assisted).