AI Annotator
OtherTextClassification
Labeled text samples by sentiment, topic, intent, and accuracy for AI training purposes. Evaluated AI responses for truthfulness, helpfulness, safety, and relevance. Compared model outputs and selected the best response based on rubric guidelines.
• Conducted sentiment, intent, and topic classification on large text datasets
• Reviewed prompts and responses for hallucinations and violations
• Provided structured feedback to improve dataset quality
• Maintained consistent standards across multiple annotation tasks
Labeled text samples by sentiment, topic, intent, and accuracy for AI training purposes. Evaluated AI responses for truthfulness, helpfulness, safety, and relevance. Compared model outputs and selected the best response based on rubric guidelines. • Conducted sentiment, intent, and topic classification on large text datasets • Reviewed prompts and responses for hallucinations and violations • Provided structured feedback to improve dataset quality • Maintained consistent standards across multiple annotation tasks
2025 - Present