AI Q and 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.