In addition to ML algorithms, future research on the optimization of organic chemistry reactions should leverage advanced AI models. In particular, we highlight large language models (LLMs) as a promising technology to enable the extraction of chemical knowledge from previously written literature. LLMs can be used to generate synthesis protocols for target materials through data mining of peer-reviewed literature . recently demonstrated an advanced LLM-powered chemistry engine called ChemCrow that is capable of planning and executing the synthesis of organic molecules. The LLM integrated 18 cheminformatic tools and performed the reasoning steps based on the information supplied by these tools to accomplish specific chemistry tasks. Along these lines, we envision that the integration of CASP tools and LLMs could accelerate the optimization of organic reactions by providing viable reaction routes with starting conditions that are close to the reaction optimum based on previous studies. Finally, LLMs could assist researchers with limited coding experience in writing the code required to automate their experimental workflows and execute their reaction optimizations.