HomeResearch LibraryPromptbreeder: Self-Referential Self-Improvement Via Pr…
system architectureChapter 5arXiv · 2023

Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution

Chrisantha Fernando (Google DeepMind), Dylan Banarse (Google DeepMind)

Abstract

We introduce Promptbreeder, a self-referential system that evolves task prompts and mutation-prompts using LLMs. Promptbreeder automatically discovers better prompts through evolutionary search.

Eigenvector Insight — Zone III / PASF-PADE AnalysisNot part of the original paper
Eigenvector Research — Marco van Hurne
How this paper contributes to solving the Zone III problem (PASF-PADE)

Promptbreeder demonstrates that agent prompts can be automatically optimized through evolutionary search. For enterprise deployments, this provides a systematic approach to improving agent performance without manual prompt engineering.

Why AI is not sufficient for Zone III without this

Zone III refers to high-complexity, high-risk, long-running agentic workflows — the class of enterprise AI deployments where a single failure can cascade across hundreds of steps. Standard AI models, trained to predict the next token, are not inherently designed for durable, governed, multi-step execution. This paper addresses one or more of the structural gaps that make Zone III deployments unsafe without explicit architectural intervention.

Key Contributions

  • →Evolutionary prompt optimization
  • →Self-referential improvement
  • →Automatic prompt discovery

Topics

prompt optimizationevolutionary algorithmsself-improvementautomatic prompting