Hybrid AI for Responsive Multi-Turn Online Conversations with Novel Dynamic Routing and Feedback Adaptation

2025-06-02

AI Summary

Enterprise-grade conversational AI systems face critical challenges including high latency, frequent hallucinations, and delayed domain knowledge updates. To address these, we propose a hybrid dialogue system that synergistically integrates retrieval-augmented generation (RAG) with intent-driven predefined responses. A novel dynamic routing mechanism adaptively dispatches queries based on semantic intent and confidence estimation. The system further incorporates dialogue state tracking, context-aware response generation, and closed-loop feedback learning to ensure multi-turn consistency and continuous improvement. It supports iterative intent evolution, online confidence-threshold tuning, and automatic expansion of response coverage. Evaluated in real-world enterprise deployments, our system achieves 95% accuracy and an average latency of 180 ms—significantly outperforming both pure RAG and pure intent-based baselines. The architecture demonstrates robustness, low-latency responsiveness, and scalable extensibility.

Technology Category

• Natural Language Processing: Conversational AI/Dialog Systems
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Retrieval-Augmented Generation (RAG) systems and large language model (LLM)-powered chatbots have significantly advanced conversational AI by combining generative capabilities with external knowledge retrieval. Despite their success, enterprise-scale deployments face critical challenges, including diverse user queries, high latency, hallucinations, and difficulty integrating frequently updated domain-specific knowledge. This paper introduces a novel hybrid framework that integrates RAG with intent-based canned responses, leveraging predefined high-confidence responses for efficiency while dynamically routing complex or ambiguous queries to the RAG pipeline. Our framework employs a dialogue context manager to ensure coherence in multi-turn interactions and incorporates a feedback loop to refine intents, dynamically adjust confidence thresholds, and expand response coverage over time. Experimental results demonstrate that the proposed framework achieves a balance of high accuracy (95%) and low latency (180ms), outperforming RAG and intent-based systems across diverse query types, positioning it as a scalable and adaptive solution for enterprise conversational AI applications.

Problem

• Addressing high latency and hallucinations in enterprise conversational AI
• Integrating frequently updated domain-specific knowledge into AI responses
• Balancing accuracy and efficiency in multi-turn online conversations

Innovation

• Hybrid framework combining RAG and intent-based responses
• Dynamic routing for complex or ambiguous queries
• Feedback loop refining intents and adjusting thresholds