RAG in ۲۰۲۵-۲۰۲۶: Applications, Evaluation, Reliability, and Emerging Architectures

Retrieval-Augmented Generation (RAG) has evolved from a relatively simple mechanism for grounding large language models in external documents into a diverse family of architectures involving hybrid retrieval, graph-based knowledge representation, multimodal retrieval, adaptive retrieval, and agent-based orchestration. Despite rapid progress, evidence remains fragmented across application domains, system designs, knowledge sources, evaluation methods, and levels of practical validation. This structured narrative review provides a comparative synthesis of a predefined corpus of ۳۰ studies published or made available during ۲۰۲۵-۲۰۲۶, including direct RAG implementations and complementary research on generative artificial intelligence, chatbot development, domain-specific language processing, evaluation, and technology adoption. Studies were analyzed according to application domain, relationship to RAG, knowledge-source design, retrieval architecture, generation strategy, evaluation methodology, reported findings, and limitations. Across healthcare, education, legal and regulatory settings, and technical applications, reliable performance was consistently associated with authoritative and well-structured knowledge sources, effective retrieval and query formulation, domain adaptation, transparent grounding, and multidimensional evaluation. Recent developments demonstrate growing interest in hybrid retrieval, GraphRAG, multi-source and multi-agent architectures, hierarchical clinical RAG, ontology-guided retrieval, and adaptive retrieval activation. However, increased architectural complexity does not guarantee reliability, and strong retrieval or generation metrics do not necessarily translate into safe, useful, or measurable real-world outcomes. Persistent challenges include retrieval failure, context noise, conversational drift, hallucination, evaluation heterogeneity, privacy and governance concerns, limited generalizability, and insufficient longitudinal validation. Future RAG research should therefore prioritize adaptive and structure-aware retrieval, source traceability, standardized multi-level evaluation, conversational robustness, human oversight, and rigorous domain-specific validation.

کلیدواژه ها:

Retrieval-Augmented Generation ، RAG Chatbots ، Large Language Models ، Information Retrieval ، Generative Artificial Intelligence ، AI Evaluation