Testing AI chatbots before launch requires a structured approach beyond simple demos. A key strategy involves creating an evaluation set of real customer questions with known correct answers and their sources. This set should cover various question types, including answerable, unanswerable, personal, and out-of-scope queries, as well as conversational follow-ups. Before the language model generates an answer, the search function that retrieves relevant documents must be tested independently to ensure accuracy. Finally, answers are graded against predefined rules, with automated grading supplemented by human review, to establish a reliable performance benchmark before deployment and for ongoing validation after changes. AI
IMPACT Establishes a framework for ensuring AI chatbot reliability and accuracy in production environments.
RANK_REASON The item discusses a methodology for testing AI chatbots, which is a practical application rather than a core AI release or research.
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