Post-processing involves refining and enhancing outputs generated by an initial computational process. In methods related to training models, it might include techniques to clean or adjust the data, remove noise, or correct inaccuracies for better performance or intelligibility. For instance, after generating text with a language model, post-processing might involve adjusting for grammatical correctness or stylistic consistency. It's closely related to preprocessing, which occurs prior to main processing.