# Active Prompting

> 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.

- Identifier: PTL-0023
- Category: Exemplars & In-Context Learning
- Canonical URL: https://protologue.com/t/active-prompting/
- Also known as: Active-Prompt
- Introduced: 2023

## 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.

## Broader terms

- [Exemplar Selection](https://protologue.com/t/exemplar-selection/)

## Related terms

- [Chain-of-Thought Prompting](https://protologue.com/t/chain-of-thought/)
- [Self-Consistency](https://protologue.com/t/self-consistency/)

## Sources

- Diao et al. (2023). Active Prompting with Chain-of-Thought for Large Language Models. https://arxiv.org/abs/2302.12246

## Cite this entry

Protologue. (2026). Active Prompting. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.0.0, PTL-0023). https://protologue.com/t/active-prompting/

License: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/)