LLM Data Labeling and Evaluation (Internal Initiative)
TextRLHF
Participated in internal LLM initiative focused on training and evaluating large language model outputs for improved accuracy and performance. Tasks included prompt engineering, response evaluation, and reinforcement learning from human feedback within proprietary workflows. Assisted in the fine-tuning of models using labeled datasets produced in-house to adapt to utility and field-specific scenarios.
• Developed prompts and evaluated AI-generated responses to align with operational requirements
• Labeled and rated output quality for model optimization and retraining cycles
• Collaborated with engineers to design annotation and feedback loops for LLM tasks
• Utilized internal tools specific to utility and energy sector adaptation
Participated in internal LLM initiative focused on training and evaluating large language model outputs for improved accuracy and performance. Tasks included prompt engineering, response evaluation, and reinforcement learning from human feedback within proprietary workflows. Assisted in the fine-tuning of models using labeled datasets produced in-house to adapt to utility and field-specific scenarios. • Developed prompts and evaluated AI-generated responses to align with operational requirements • Labeled and rated output quality for model optimization and retraining cycles • Collaborated with engineers to design annotation and feedback loops for LLM tasks • Utilized internal tools specific to utility and energy sector adaptation
2024 - Present