Hybrid AI for Responsive Multi-Turn Online Conversations with Novel Dynamic Routing and Feedback Adaptation (2025)

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.

## introduction recent progress in nlp has drastically changed the landscape of conversational ai, and among such new state-of-the-art solutions, a class of retrieval-augmented generation (rag) systems has emerged. by combining large language models (llms) with separate information retrieval pipelines, rag systems can generate contextually rich and factually grounded responses, which are necessary for knowledge-intensive applications @xcite . however, enterprise-scale conversational ai systems often face real-world challenges such as diverse user query patterns, varying levels of query complexity, and stringent la-tency requirements for seamless user experiences. high computational costs, susceptibility to hallucinations when retrieval is misaligned, and inefficiencies in managing frequently updated domainspecific knowledge further compound these challenges, particularly in dynamic environments like customer support @xcite . in practice, ensuring that such systems can scale while maintaining accuracy and low latency remains an industry pain point. in contrast, classical intent-based chatbots are efficient in processing frequently asked questions (faq) and other predictable queries, thanks to using pre-defined responses. their lightweight computational footprint and scalability also make them well-suited for high-confidence, domain-specific scenarios @xcite . however, these systems are inherently rigid and often struggle with query diversity, especially when faced with ambiguous or context-dependent user interactions. in high-demand enterprise settings, the inability of intent-based systems to adapt quickly to evolving user needs or handle complex multi-turn dialogues @xcite ) results in inconsistent user experiences and increased operational overhead for manual updates. the inability to balance adaptability with efficiency underscores the need for hybrid systems that synergize the strengths of rag and intent-based approaches. in order to solve these challenges, we propose a novel hybrid framework that combines rag systems with intent-based canned responses for dynamic, multi-turn customer service interactions. while prior works have explored combining rag and intent-based systems independently, our contribution lies in a cohesive framework that not only integrates these elements but also introduces a dynamic confidence-based routing mechanism refined through user feedback. this mechanism ensures that query routing decisions are continuously op-timized based on real-time user interactions, enabling a system that evolves and adapts without manual intervention. additionally, our framework addresses scalability challenges by efficiently balancing computational resources, making it particularly suited for enterprise-scale applications where latency and accuracy are paramount. our approach utilizes a dynamic query routing mechanism that evaluates the intent confidence level of user queries: the framework is further enhanced with a dialogue context manager, keeping track and managing evolving intents across multiple turns, ensuring consistent and coherent interactions. additionally, a feedback loop continuously refines the intent repository, adapting to emerging user needs and expanding response coverage over time. our system is designed to meet enterprise latency standards, delivering responses within an acceptable threshold (sub-200ms latency and high turn efficiency), thereby ensuring user engagement and satisfaction in real-time applications @xcite . ## proposed framework the proposed framework integrates the efficiency of intent-based canned responses with the con-textual richness and adaptability of retrieval-augmented generation (rag) systems @xcite . by dynamically routing queries based on intent confidence and leveraging user feedback for adaptive refinement, the framework addresses latency, accuracy, and scalability challenges while maintaining coherence across multi-turn interactions. figure 1 illustrates the architecture with key modules, data flow and a redis cache which stores frequently accessed intents and responses for faster retrievals. ## experiment and results the hybrid framework was evaluated on four metrics: accuracy, response latency, cost efficiency, and turn efficiency. these evaluations spanned inhouse datasets of live customer queries, and scalability tests. ## conclusion we proposed a hybrid conversational framework that integrates intent-based canned responses with retrieval-augmented generation (rag) systems, explicitly designed to handle multi-turn interactions. the framework dynamically routes queries based on intent confidence, ensuring low latency for predefined intents while leveraging rag for complex or ambiguous queries. the inclusion of a dialogue context manager guarantees coher-ence across multi-turn interactions, and a feedbackdriven mechanism continuously refines intents and confidence thresholds over time. experimental results demonstrated the proposed framework's ability to balance accuracy (95%), response latency (180ms), and cost efficiency (0.7), while achieving superior context retention and turn efficiency in multi-turn scenarios. the system effectively handles multi-turn dialogues with minor limitations in long conversations exceeding 10 turns. our contributions include a scalable, adaptive solution for dynamic conversational ai, addressing key industry challenges such as query diversity, evolving knowledge bases, and real-time performance requirements. future research will focus on enhancing multi-turn context management, conducting ablation studies to isolate module contributions, and exploring real-time learning mechanisms for continuous adaptation. this work advances the state-of-the-art in enterprise conversational ai, providing a robust framework for handling complex, multi-turn interactions efficiently. while our system demonstrates strong performance in enterprise customer support scenarios, it is optimized for english language applications and may require adaptation for multilingual deployments. expanding to other languages introduces challenges such as acquiring labeled training data and handling linguistic variations, which may increase operational costs and training time. additionally, our intent classifier is trained on domain-specific datasets, and extending to new domains or industries will necessitate retraining with relevant data, impacting both cost and deployment timelines. lastly, integrating real-time learning mechanisms that adapt continuously without periodic retraining is an avenue for future exploration, providing a more seamless and cost-effective method for maintaining system relevance over time. future work will also include studies to isolate the impact of the dialogue context manager and quantify its contribution to system performance, as well as extending our framework to support multilingual conversations by improving intent recognition and retrieval efficiency across diverse languages. new intents are generated from user feedback and query patterns, which are processed offline to update the intent data store. intent classification is refined continuously by an adaptive system feedback loop. unresolved queries are logged, clustered on the basis of semantic similarity, and flagged for review. when a cluster reaches a certain size, a new intent is created offline and integrated into the classifier. additionally, confidence thresholds are periodically adjusted based on user feedback to improve the routing of ambiguous queries.