AI Response Evaluation & Structured Feedback
Contributed to AI training and evaluation workflows focused on improving large language model performance through structured human feedback. Tasks involved reviewing AI-generated responses to a wide range of prompts, evaluating outputs for factual accuracy, logical consistency, instruction adherence, tone, and completeness based on detailed rubrics. Provided written justifications explaining evaluation decisions, identified reasoning flaws or hallucinations, and suggested concrete corrections or improvements to align responses with user intent. Work required careful attention to nuanced instructions, domain knowledge in computer science and data systems, and consistent application of quality standards. Emphasis was placed on clarity, precision, and reliability of feedback to support reinforcement learning from human feedback (RLHF) and supervised fine-tuning processes.