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Claude Code can auto-iterate prompts by running evals, rewriting, and keeping winners, boosting a hook-writer skill from 32/50 to 47/50 overnight.
GEPA is an open-source tool that automatically optimizes prompt instructions using execution traces and scores, raising Claude Haiku 4.5's pass rate from 65% to 85% without requiring a model swap.
A post reflecting on the DSPy framework's architecture built around signatures, modules, and optimizers, and noting its continued growth since 2022.
Researchers introduce BEHEMOTH benchmark and CluE cluster-based prompt optimization to enable LLMs to extract and retain heterogeneous memory across diverse tasks, achieving 9% gains over prior self-evolving frameworks.
This article introduces AdamOpt, an open-source tool based on 'Adam's Law' that optimizes prompts by replacing low-frequency words with high-frequency synonyms to reduce perplexity. It highlights the tool's bilingual support, offline capability, and practical performance improvements in text generation.
The paper introduces mmGRPO, a multi-module extension of Group Relative Policy Optimization (GRPO) that improves accuracy in modular AI systems by optimizing language model calls and prompts. It reports an average 11% accuracy improvement across various tasks and provides an open-source implementation in DSPy.
This paper introduces Self-Supervised Prompt Optimization (SPO), a framework that optimizes prompts for LLMs without external references by using output comparisons, significantly reducing costs and data requirements.
Evolver is a GEP-powered self-evolution engine for AI agents that automates prompt optimization and creates auditable, reusable evolution assets. The project is transitioning from fully open source to source-available while maintaining backward compatibility with existing MIT and GPL-3.0 releases.