A 5-15% average increase may seem modest, but automation means scaling, and that's the real killer move.

AIMPACT reports that Amazon scientists have proposed an automated prompt engineering framework called Promptimus, which can improve high-quality LLM prompts without human intervention. By iteratively optimizing strategies and auxiliary optimizer models to analyze the interaction between prompts and model outputs, it automatically adjusts aspects such as instruction clarity and example selection. Multiple benchmarks show an average improvement of 5-15%, with GSM8K math reasoning increasing from 78% to 85%, covering tasks like commonsense question answering and code generation. The framework is versatile, not dependent on specific LLM architectures or tasks, and uses regularization and cross-validation to prevent over-optimization, ensuring generalization ability.