Although Differential Evolution (DE) remains a fundamental optimization technique, its mutation strategies often strive to strike a balance between exploration and exploitation in high-dimensional, multimodal environments. Employing the paradigm of LLM-assisted optimization algorithm generation, this paper presents a novel mutation that is synthesized using a methodology that exploits Large Language Models (LLMs) for automated strategy design. An LLM is provided with a guiding prompt to produce these mutation heuristics. The stages of this prompt’s structure take into account historical DE variants, problem characteristics, and evolutionary principles, and the resulting mutation and Differential Evolution algorithm are extensively benchmarked on the Black-Box Optimization (BBO) suite for 10-, 30-, and 40-dimensional spaces. When incorporated into DE, the LLM-generated mutation continuously outperformed state-of-the-art variants (which included DE/rand/1 & 2, DE/best/1 & 2, DE/current-to-rand/1, JADE, and Union differential evolution mutation) across 30 independent runs per function. It emerged first in all tested configurations (10D, 30D, and 40D) and earned the best average ranking. Statistically significant differences between the analyzed algorithms are indicated by the obtained p-values (3.96E−18, 5.76E−19,and 2.36E−20), which demonstrates the superiority of the outlined strategy and reinforces it’s effectiveness and dependability. Together with DE, this new mutation operator unlocks a window to AI-enhanced evolutionary algorithm engineering, which promises scalable advancements in Black-Box optimization tasks.