AI Output Evaluation Practice (Relevant Project)
Rated and labeled AI output examples based on clarity, correctness, relevance, and tone as part of RLHF practice. Ranked responses and recorded preference-style labels to reflect quality differences. Used categorical quality labels with explanations to support training signals for machine learning workflows.• Labeled over 150 text examples for RLHF-style clarity/correctness/relevance/tone criteria.• Performed response ranking to generate preference signals.• Applied quality categories (poor/good/excellent) with written rationales.• Documented labeling outcomes to support downstream evaluation and training exercises.