A Survey of conversational AI from rule based to generative and retrieval augmented generation chatbots
Abstract
Retrieval-Augmented Generation (RAG) has rapidly emerged as a foundational paradigm for knowledge-grounded conversational agents, particularly in high-stakes domains where factual accuracy, transparency, and adaptability are paramount. Although prior surveys have examined the evolution of chatbots or the mechanics of retrieval-augmented language models in isolation, a comprehensive, system-level synthesis of RAG-based chatbots as end-to-end conversational architectures remains limited. This survey addresses that gap by presenting a structured, design-oriented analysis of RAG-driven conversational systems through a principled framework that decomposes architectures along critical dimensions, including document segmentation and chunking strategies, embedding and indexing mechanisms, retriever and re-ranking models, knowledge integration and grounding techniques, attribution mechanisms, and evaluation methodologies. Following a transparent and reproducible literature selection protocol, we examine representative RAG implementations across both open-domain and specialized settings, systematically analyzing architectural trade-offs, interaction effects among components, recurring failure modes, and sources of performance variability that are often obscured by aggregate benchmark metrics. Beyond cataloguing techniques, the survey advances a conceptual understanding of when and why RAG systems succeed or degrade, critiques prevailing evaluation practices, and delineates emerging…
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