Data Annotation — Prompt Engineer (LLM QA and training-data feedback)
Provided quality assurance for multiple Large Language Models by reviewing their accuracy, written quality, and adherence to safety guidelines. Used detailed rubrics to evaluate model outputs for research-intense tasks and ensure they met specified criteria. Contributed to improving model performance by validating and refining the training datasets based on review findings. • Reviewed LLM outputs for accuracy and written quality • Assessed compliance with safety guidelines • Completed highly detailed rubrics for research-intensive tasks • Produced feedback used to improve training data and model application for Google Workplace Software