Data Structuring & Classification Project
Project Scope: Contributed to AI model training initiatives focused on improving large language model (LLM) accuracy, response quality, and contextual understanding. The project involved annotating and evaluating structured and unstructured text datasets to support supervised machine learning workflows and performance optimization. Performed text classification based on predefined taxonomies, Annotated datasets for sentiment, intent, and topical relevance Evaluated AI-generated responses for factual accuracy, coherence, and guideline compliance. project size. Handled hundreds to thousands of data entries per project cycle. Quality Measures Adhered To: Followed strict annotation guidelines and task-specific rulebooks Maintained high inter-annotator agreement standards