Miscellaneous
Yeoman's Work
In AI contexts, yeoman's work refers to the unglamorous but essential tasks that underpin AI systems: data labeling, data cleaning, annotation quality review, prompt testing, and evaluation. These tasks require sustained effort and attention to detail.
High-quality training data—produced through disciplined yeoman's work in annotation and curation—is consistently shown to improve model performance more than architectural innovations. The AI field often undervalues this work relative to research contributions.
Authority Links
Related Terms
Miscellaneous
Training Data
The labeled or unlabeled dataset used to fit a model's parameters during the learning process.
Miscellaneous
Dataset
An organized collection of data examples prepared for training, evaluating, or testing AI models.
Miscellaneous
Label
Annotation indicating the correct output or category for a training example in supervised learning.
Miscellaneous
Data Science
Interdisciplinary field combining statistics, programming, and domain knowledge to extract insights from data.