A new active learning method developed by Google significantly reduces the amount of training data required for fine-tuning large language models (LLMs) while enhancing alignment with human expert evaluations. This scalable curation process allows for the identification of the most informative examples and achieves up to a 10,000x reduction in training data, enabling more effective responses to the evolving challenges of ad safety content classification.