AI-Driven Nutrition Assistant (Self-initiated) — Data annotation and response evaluation
Collected and annotated nutritional data to create a knowledge base for a specialized nutrition AI assistant. Evaluated model responses rigorously to verify scientific accuracy and adherence to safety guidelines. Applied testing workflows to ensure the assistant outputs reliable and compliant nutritional advice. • Annotated nutritional data to support domain knowledge retrieval • Conducted response evaluation against scientific correctness criteria • Checked adherence to safety guidelines for nutritional recommendations • Iterated on evaluation results to improve quality and trustworthiness