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E-Commerce Chatbot Score Rises from 86 to 91 After Targeted System Prompt Fixes

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A team at BotCritic audited an e-commerce customer support chatbot by testing it against four simulated customer personas covering order tracking, returns, refunds, and product queries, scoring it 86 out of 100 on the first run. The audit identified four specific flaws: the bot asked for order numbers it could not actually look up, omitted item-condition requirements from return explanations, gave an ambiguous refund trigger, and failed to proactively warn users who may have exceeded the 30-day return window. Developers addressed all four issues by updating only the bot's system prompt, with no other changes to the underlying model or configuration. Re-running the identical test suite produced a score of 91 out of 100, with the largest improvement seen in the Robustness category, which rose from 78 to 88. The exercise highlights how precisely targeted prompt changes can meaningfully improve chatbot performance without requiring deeper technical overhauls.

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