Customer service often relies on human agents, which, while effective, can be costly and slower to scale. Recent advancements in intelligent chatbots, particularly Retrieval-Augmented Generation (RAG) models, have significantly enhanced efficiency by integrating large language models with external knowledge retrieval. However, developing a multi-turn RAG-based chatbot for real-world customer service presents additional complexities, requiring components like adaptive retrieval and query reformulation. These components typically require substantial annotated data, which is often scarce. To overcome this limitation, we propose methods to automatically generate labels for these components using real customer-agent dialogue data. Specifically, we introduce two labeling strategies for adaptive retrieval: an intent-guided strategy and an explanation-based strategy, along with two query reformulation strategies: natural language query reformulation and keyword-based reformulation. Our experiments reveal that the explanation-based strategy yields the best results for adaptive retrieval, while the keyword-based reformulation improves document retrieval quality.Our findings offer valuable insights for practitioners working on multi-turn RAG systems.

Accurate intent classification is critical for efficient routing in customer service, ensuring customers are connected with the most suitable agents while reducing handling times and operational costs. However, as companies expand their product lines, intent classification faces scalability challenges due to the increasing number of intents and variations in taxonomy across different verticals. In this paper, we introduce REIC, a Retrieval-augmented generation Enhanced Intent Classification approach, which addresses these challenges effectively. REIC leverages retrieval-augmented generation (RAG) to dynamically incorporate relevant knowledge, enabling precise classification without the need for frequent retraining. Through extensive experiments on real-world datasets, we demonstrate that REIC outperforms traditional fine-tuning, zero-shot, and few-shot methods in large-scale customer service settings. Our results highlight its effectiveness in both in-domain and out-of-domain scenarios, demonstrating its potential for real-world deployment in adaptive and large-scale intent classification systems.