AI Training Data Annotation & Content Structuring System
Developed and managed a structured data annotation system to support AI-generated text outputs across short-form storytelling and factual content pipelines. Designed labeling frameworks to categorize and tag textual data into defined components such as hooks, narrative progression (buildup, climax, twist), and content types (e.g., shocking facts, legal anomalies, cultural insights). This ensured consistency, clarity, and alignment with desired output styles. Performed iterative annotation and quality control on AI-generated responses, refining labeling rules and input structures (prompt engineering) to improve accuracy, engagement, and coherence. Implemented repeatable workflows using automation tools (e.g., n8n) to standardize data processing and maintain scalability.