AI Answer Quality Evaluator for RAG Product
Evaluated the quality of answers generated by a Retrieval-Augmented Generation (RAG) enterprise knowledge base AI solution. Assessed responses for relevance, factual consistency, proper context usage, and business usefulness. Focused on improving answer accuracy, traceability, and alignment with enterprise requirements. • Reviewed AI-generated responses as part of iterative product optimization • Applied technical judgment to identify shortcomings and suggest workflow improvements • Used search systems and document retrieval as context in assessments • Integrated evaluation into development lifecycle and product quality metrics