实时识别客户问题，精准匹配答案，提升客服响应效率
Beyond-RAG: Question Identification and Answer Generation in Real-Time Conversations
- 先识别问题是否为常见问答，直接调用FAQ库
- 非常见问题则用RAG生成答案，响应时间<2秒
- 适合需要低延迟的智能客服场景
在客户联络中心，人工客服常因需手动解析问题并查找知识库而面临过长的平均处理时长（AHT）。尽管基于大语言模型（LLM）的检索增强生成（RAG）系统已被广泛用于辅助，但在实时对话中仍存在查询表述不准、频繁重复检索常见问题（FAQ）等问题。为此，我们提出一种决策支持系统，可超越传统RAG：首先实时识别客户问题；若匹配现有FAQ，则直接从FAQ数据库获取答案；否则通过RAG生成回答。该系统减少对人工查询的依赖，确保响应在2秒内完成。该方案已部署于Minerva CQ的AI辅助人机协同解决方案中，显著提升效率、降低AHT与运营成本。此外，我们还设计了一种自动化LLM代理工作流，可在无预定义FAQ时，从历史通话记录中自动提取高频问题。
原文摘要 · Abstract (English)
In customer contact centers, human agents often struggle with long average handling times (AHT) due to the need to manually interpret queries and retrieve relevant knowledge base (KB) articles. While retrieval augmented generation (RAG) systems using large language models (LLMs) have been widely adopted in industry to assist with such tasks, RAG faces challenges in real-time conversations, such as inaccurate query formulation and redundant retrieval of frequently asked questions (FAQs). To address these limitations, we propose a decision support system that can look beyond RAG by first identifying customer questions in real time. If the query matches an FAQ, the system retrieves the answer directly from the FAQ database; otherwise, it generates answers via RAG. Our approach reduces reliance on manual queries, providing responses to agents within 2 seconds. Deployed in AI-powered human-agent assist solution at Minerva CQ, this system improves efficiency, reduces AHT, and lowers operational costs. We also introduce an automated LLM-agentic workflow to identify FAQs from historical transcripts when no predefined FAQs exist.
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