Building Reflective Prompt Optimization with GEPA: Multi-Component Prompts, Structured Feedback, and Held-Out Validation
Researchers have developed GEPA, a framework for optimizing language model prompts, particularly for arithmetic word problems. This method involves starting with a basic prompt and iteratively refining it using a structured feedback loop. GEPA employs a multi-component approach where both instructions and output format rules evolve together, validated against a held-out dataset to measure performance improvements. AI
IMPACT This framework offers a structured method for improving LLM performance on specific tasks through automated prompt refinement.