AI Collaboration & Output Verification (RLHF Focus)
Contributed to AI collaboration workflows with an RLHF focus by iteratively prompting large language models to generate and refine front-end code and mathematical logic. Performed systematic evaluation of AI outputs for syntax accuracy, logical coherence, and edge-case handling, explicitly correcting hallucinations and inefficiencies. Translated abstract spatial concepts into structured, machine-readable rules for complex data engines. • Used LLMs such as Copilot and Gemini to produce and refine code and frameworks. • Verified generated scripts against programming and logic correctness criteria. • Debugged and repaired AI output issues including hallucinations and inefficiencies. • Structured logic rules for data engine execution (e.g., if/then pathways).