AI Model Training & Data Labeling Project
Project Scope The project focused on improving the accuracy, relevance, and safety of AI models through high-quality data labeling and evaluation. The scope included preparing structured and unstructured data for machine learning training, validating AI-generated outputs, and ensuring consistency across large datasets. Work was performed under strict annotation guidelines to support supervised learning and reinforcement learning workflows. Specific Data Labeling & Annotation Tasks Performed Text data labeling and classification Prompt–response evaluation and ranking Intent recognition and category tagging Sentiment and tone labeling Content relevance and factual accuracy review Identification and correction of AI output errors Safety and policy compliance tagging Quality review of peer-labeled data Tools & Workflow Web-based annotation and AI training platforms Quality assurance dashboards Task queues and feedback systems Version-controlled guidelines and documentation