Hybrid Retrieval-Augmented Generation (RAG) System for Intelligent Question Answering

Retrieval-Augmented Generation (RAG) has emerged as an effective approach for improving the accuracy and reliability of Large Language Models (LLMs) by integrating external knowledge retrieval with text generation. However, traditional RAG systems often suffer from limitations such as static retrieval mechanisms, poor context adaptation, and reduced efficiency when handling complex queries. To address these challenges, this paper proposes an Adaptive Hybrid Retrieval-Augmented Generation Framework for context-aware question answering and intelligent response generation.

The proposed system combines sparse retrieval techniques using BM25 with dense retrieval through vector-based semantic search to improve document relevance and retrieval accuracy. An adaptive retrieval mechanism dynamically selects suitable retrieval strategies based on query complexity and contextual requirements. Retrieved documents are further processed through a context fusion and ranking module to construct high-quality contextual information for the Large Language Model. The framework supports both offline and online deployment modes, enabling efficient operation in local as well as cloud-based environments.

The proposed Hybrid RAG architecture enhances semantic understanding, reduces hallucination, improves response consistency, and provides real-time access to updated knowledge sources. Experimental analysis demonstrates improved retrieval efficiency, response relevance, and scalability compared to traditional retrieval-generation approaches. The system can be effectively applied in intelligent chatbots, domain-specific question answering, educational assistants, healthcare information systems, and enterprise knowledge management applications.