The effectiveness of these methods was first tested on α-pinene due to its high predicted reactivity score (0.87) and selectivity (0.90), making it an ideal candidate for methodological comparison. Over the course of 455 reactions, distributed across 10 iterative rounds per method within the search space (Table ), we closely monitored the improvement in yield (Figure ). Our findings indicate that the random method stagnated at low yields (around 20%) even after extensive iteration, underscoring the inefficiency of non-guided experimental approaches. While there is excitement and interest in using LLMs for synthesis optimization, it is important to understand that their "suggestions" are not based on statistical learning and lack a mathematical foundation. Instead, they make educated guesses based on observed general trends and domain knowledge, and usually change one factor at a time (Figures and). However, this does not mean that LLMs are not valuable for synthesis optimization; on the contrary, it was demonstrated that the integrated LLM-ML approach can start with an LLM-informed search space that incorporates both literature-derived insights and empirical data. This method rapidly refined the reaction conditions through ML algorithms. In this case, LLM relies on the output from ML models to make decisions rather than having an educated guess on what the next conditions should be tried. This synergy also enabled the precise tuning of conditions to achieve yields over 60% (Figure and Section S6 of the Supporting Information). Building on the success with α-pinene, we applied the LLM-ML framework to optimize the synthesis conditions for eight additional substrates (Table ) with a batch size of four for 10 iterations to demonstrate its generalizability. Notably, eight substrates were identified using the reactivity and selectivity models (Figure ), which provide chemists with not only a numerical value indicating the likelihood of oxidation under C-H oxidation but also visualizations of the substructures contributing to oxidation, making the decision-making process more transparent. The selectivity model also indicates which sites are likely to be oxidizable.