To further our exploration into the synthesis optimization of electrochemical C-H oxidation, we developed and compared several methodologies on the same electrochemical synthesis platform mentioned in the prior section. Our focus shifted towards active learning strategies designed to iteratively refine synthesis conditions to maximize yield while minimizing experimental iterations. We used a batch approach on the screening electrochemical reactor with each batch comprising 3 to 5 reactions, analyzed and adjusted based on the NMR yield outcomes to guide subsequent experimental conditions (Figure ). We developed and examined four distinct strategies: (i) random sampling, representing traditional trial-and-error; (ii) LLM-driven prediction , which mimics the decision-making process by human chemist and leverages chemical intuition without statistical learning; (iii) ML-only optimization , which applies a purely statistical approach devoid of initial chemical insight; and (iv) a hybrid LLM-ML approach, where LLMs guide the initial parameter selection, subsequently LLMs use ML as helper functions to make suggestions on synthesis parameters.