Optimization by Prompting
Also called OPRO, LLMs as optimizers.
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.
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.
Sources
- Yang et al. (2023). Large Language Models as Optimizers.
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/
BibTeX
@misc{protologue_opro,
title = {Optimization by Prompting},
author = {{Protologue}},
year = {2026},
howpublished = {Protologue: A Taxonomy of Prompting and LLM Techniques, v1.0.0},
note = {Entry PTL-0074},
url = {https://protologue.com/t/opro/}
}