@shawntenam: GEPA (http://github.com/gepa-ai/gepa) bumped Haiku 4.5 from 65% to 85% pass rate by auto-optimizing my prompt instructi…
Summary
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.
Similar Articles
FAPO: Fully Autonomous Prompt Optimization of Multi-Step LLM Pipelines
FAPO is a framework for fully autonomous prompt optimization of multi-step LLM pipelines, combining prompt editing and structural changes. It outperforms the GEPA baseline in 15 of 18 comparisons, with gains up to +33.8 pp on security tasks.
GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning
GEPA is a prompt optimizer that uses natural language reflection to learn from trial and error, outperforming reinforcement learning methods like GRPO and MIPROv2 with up to 35x fewer rollouts across multiple tasks.
@harold_matmul: it was my idea :) Using GEPA is a very natural workflow for creating LLM programs. The iteration speed is very quick, a…
A user thanks for the GEPA tool, highlighting its natural workflow for LLM programs, fast iteration, and ability to bias optimization with data-derived priors.
@leanxbt: https://x.com/leanxbt/status/2070852461494202609
A detailed article introducing Loop Prompt Engineering, a method to automate prompt optimization by iteratively rewriting prompts based on evaluation against a dataset, with emphasis on avoiding recursive traps.
@harold_matmul: dspy.GEPA used in pretraining data curation in the new Microsoft AI effort :-)
The article explains how GEPA (Genetic-Pareto Optimization) within DSPy is used for efficient prompt tuning, specifically applied to pretraining data curation at Microsoft AI, allowing researchers to replace manual prompt engineering with automated compute-driven optimization.