LLM Output Evaluation and Structured Data Review
During my frontend development work, I have consistently evaluated and improved system outputs in real-world applications. This involved reviewing API responses, validating logic, testing edge cases, and ensuring system responses met functional and user requirements. I worked with structured and semi-structured data, identifying inconsistencies, debugging incorrect outputs, and documenting issues clearly. My workflow included assessing response quality, correctness, and usability, which aligns closely with LLM evaluation and reinforcement learning from human feedback (RLHF). I also performed prompt-response testing, reviewed system-generated outputs, and provided structured feedback to improve performance and reliability. This experience has strengthened my ability to follow detailed guidelines, maintain consistency, and apply analytical reasoning in evaluation tasks. I am now focused on applying these skills to AI training, response ranking, coding evaluation, and model alignments