Practicing AI data annotation tasks (text labeling/dataset categorization) including prompt testing and AI response evaluation
Conducts ongoing AI response evaluations focused on accuracy, relevance, and clarity for AI-generated outputs. Reviews datasets for errors, duplicates, and inconsistencies to improve dataset quality for model training. Develops and applies prompt-based testing methods to understand how prompts affect output quality. • Evaluates AI responses against defined quality criteria • Detects and flags dataset quality issues such as duplicates and inconsistencies • Writes structured prompts to test expected model behavior • Studies annotation guidelines and machine learning data workflows