To address this challenge, we employed semantic analysis using LLM agents guided by human instructions. This approach allows for precise extraction of relevant data by understanding and interpreting the context within scientific manuscripts. Specifically, the LLMs were tasked with identifying papers that met three critical criteria for our electrochemical oxidation dataset: (1) the paper must be an experimental study on electrochemical synthesis (i.e., not a review paper or computational study), ( ) it must involve C-H bond oxidation to alcohol or ketone products (i.e., not other types like C-C coupling), and (3) the reaction must occur on an aliphatic carbon (i.e., not a C(sp 2 )-H) (Section S3.2 of the Supporting Information). In essence, the LLMs function as a customized filter, guided by human language instructions, that automates the process of reading through each paper, understanding not only the experimental sections but also the discussions, and selecting the qualifying papers. To evaluate the performance, we analyzed 140 relevant papers using pre-designed prompts (Table ). The LLMs took approximately 15 seconds per manuscript to assign a Yes/No label, resulting in a total analysis time of about 35 minutes for all selected literature (Figure ). Validation against ground truth revealed that the LLMs achieved 96% accuracy, correctly identifying 21 relevant papers containing 497 reactions for 247 substrates (Figure ).