Advanced Data Evaluation & Prompt Optimization — Multimodal Prompt Labeling (Visual Taxonomy)
Engineered a structured labeling taxonomy for multimodal visual AI engines to train more consistent, studio-grade cinematic outputs. Labeled and defined visual attributes spanning subject details, lens physics, lighting geometry, and color profiles to support model learning. Applied the taxonomy as part of prompt labeling and output-constraint alignment for improved generation quality. • Created a 5-part taxonomy for visual attribute labeling • Defined labels for subject details, lens physics, lighting geometry, and color profiles • Prepared labeled prompts/tags to train multimodal visual consistency • Supported constraint enforcement via taxonomy-driven prompt labeling