AI Q&A Annotation for Structured Account Management Data
Contributed to a structured AI training project focused on generating and validating natural-language Q&A pairs based on synthetic account management database environments. Responsibilities included: Interpreting SQL query outputs and transforming them into clear, realistic user-facing questions and answers. Ensuring strict logical alignment between structured database results and textual responses. Creating business-context scenarios involving customers, transactions, balances, account activity, and performance summaries. Identifying inconsistencies in model-generated responses and documenting reproducible error patterns. Applying quality control standards to maintain linguistic clarity, contextual accuracy, and data integrity. Reviewing outputs for ambiguity, hallucination, and reasoning flaws. Project involved high-volume annotation tasks requiring precision, consistency, and structured reasoning. Additional Information Maintained strong adherence to annotation guidelines