LLM Output Evaluator and AI Training Data Creator
Conducted structured evaluation of LLM model outputs, identifying model reasoning gaps and documenting correctness failures. Applied structured QA scoring rubrics to systematically rate and classify AI-generated responses. Produced findings directly usable as high-quality AI agent training data for further model fine-tuning and integrity. • Evaluated AI agent and LLM responses in technical domains such as code analysis and correctness. • Utilized detailed QA scoring approaches to ensure high standard of annotation quality. • Documented and categorized AI failure cases with clear rationale for model improvement. • Generated structured training data for downstream AI and LLM workflows.
Conducted structured evaluation of LLM model outputs, identifying model reasoning gaps and documenting correctness failures. Applied structured QA scoring rubrics to systematically rate and classify AI-generated responses. Produced findings directly usable as high-quality AI agent training data for further model fine-tuning and integrity. • Evaluated AI agent and LLM responses in technical domains such as code analysis and correctness. • Utilized detailed QA scoring approaches to ensure high standard of annotation quality. • Documented and categorized AI failure cases with clear rationale for model improvement. • Generated structured training data for downstream AI and LLM workflows.
2025 - Present