protologue

Prompt Optimization

Also called automatic prompt optimization, automated prompt engineering.

Prompt optimization is the automatic search for a better prompt, in which a language model proposes candidate instructions or examples and each candidate is scored on an evaluation set until performance stops improving.

Description

Methods differ in how they propose changes. Some resample paraphrases, as in APE, some show the optimizer a history of scored prompts, as in OPRO, some critique errors in natural language and edit accordingly, as in ProTeGi and TextGrad, and some use evolutionary search, as in Promptbreeder and EvoPrompt. All of them depend on a representative evaluation set and metric, and can overfit to it.

Sources

  1. Schulhoff et al. (2024). The Prompt Report: A Systematic Survey of Prompt Engineering Techniques.
  2. Pryzant et al. (2023). Automatic Prompt Optimization with "Gradient Descent" and Beam Search.

Cite this entry

Protologue. (2026). Prompt Optimization. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.4.0, PTL-0150). https://protologue.com/t/prompt-optimization/

BibTeX
@misc{protologue_prompt_optimization,
  title = {Prompt Optimization},
  author = {{Protologue}},
  year = {2026},
  howpublished = {Protologue: A Taxonomy of Prompting and LLM Techniques, v1.4.0},
  note = {Entry PTL-0150},
  url = {https://protologue.com/t/prompt-optimization/}
}

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