STEM与日常科技·英语精读30篇（5）
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RAG Failures in Customer Service Chatbots: When Retrieval Misleads Generation
客服聊天机器人中的RAG失效：检索环节如何误导生成结果
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Retrieval-Augmented Generation systems retrieve policy documents before generating replies, yet outdated PDFs often rank higher than revised web content due to legacy metadata.
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A query about 'return window extension' may retrieve a discontinued holiday policy instead of current standard terms, causing misleading confidence in the response.
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Semantic similarity scoring fails when product names change—'Galaxy S23 FE' and 'Galaxy S23 Fan Edition' yield low match scores despite identical coverage.
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Users reporting billing issues frequently trigger irrelevant warranty clauses because retrieval engines over-index on financial keywords like 'charge' or 'refund'.
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Post-hoc audit logs reveal that 68% of incorrect RAG outputs stem from retrieval top-k contamination—not generation hallucination.
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Effective mitigation combines timestamp-aware ranking, domain-specific query rewriting, and explicit uncertainty signaling in low-confidence retrieval contexts.