# Optimization by Prompting

> Optimization by PROmpting (OPRO) uses a language model as an optimizer, giving it a meta-prompt containing previously tried prompts and their scores and asking it to propose a better prompt, repeating over many rounds.

- Identifier: PTL-0074
- Category: Prompt Optimization
- Canonical URL: https://protologue.com/t/opro/
- Also known as: OPRO, LLMs as optimizers
- Introduced: 2023

## Description

OPRO found instructions such as "Take a deep breath and work on this problem step-by-step" that improved math benchmark accuracy for the model being optimized.

## Related terms

- [Automatic Prompt Engineer](https://protologue.com/t/automatic-prompt-engineer/)
- [DSPy](https://protologue.com/t/dspy/)

## Sources

- Yang et al. (2023). Large Language Models as Optimizers. https://arxiv.org/abs/2309.03409

## Cite this entry

Protologue. (2026). Optimization by Prompting. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.0.0, PTL-0074). https://protologue.com/t/opro/

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