Design and Implementation of a Retrieval-Augmented Generation (RAG) Based Chatbot for Document Question Answering

Abstract

: With the rapid growth of enterprise databases, personal cloud storage, and corporate digital archives, the number of unstructured text documents requiring manual analysis and reading has increased massively. Extracting relevant information from massive multi-page documents manually is extremely time-consuming and prone to human oversight. Traditional keyword-based search systems and earlier rule-based chatbots do not really work that well anymore because they lack deep semantic understanding and fail to synthesize context-aware answers to user queries. This paper mainly talks about how Retrieval-Augmented Generation (RAG) frameworks are making document-based question-answering smarter and more reliable than traditional models. It explores different types of RAG techniques like dense retrieval, vector database optimization, generative language model integration, and hybrid search frameworks... and explains how they are implemented in real-world intelligent systems. The paper also discusses what these methods are good at, where they struggle a bit, and how practical they actually are when it comes to handling complex, multi-modal documents. One important thing the study shows is that RAG-based document chatbots can achieve contextual response accuracy levels of more than 95% and can reduce language model hallucinations quite a lot. Still, some problems are not fully solved yet — especially things related to processing nested tables within documents, sub-optimal retrieval latency, and parsing highly technical schemas in real time. Because of this, future research is expected to focus more on Graph-based RAG (GraphRAG), agentic multi-step reasoning, and privacy-preserving federated retrieval systems that can scale up dynamically as large document corpora evolve.