Citation:

Sumeet Mathur, 2026. "Advances and Challenges in Retrieval-Augmented Generation Models for Knowledge-Driven NLP Tasks", ESP Journal of Engineering & Technology Advancements  6(2): 67-75.

Abstract:

The advancement in artificial intelligence continues to transform knowledge-based applications, and Retrieval-Augmented Generation (RAG) has become a prominent framework for this activity. By adding wide-scale information retrieval, RAG enhances huge language models, allowing them to produce responses based on applicable and current knowledge instead of simply using the pre-trained memory. Originally intended as open-domain question answering, RAG has since been applied in general domains such as healthcare, legal reasoning, education, enterprise analytics and conversational AI. RAG enhances the accuracy of facts, relevant to the context and intelligibility by retrieving domain-specific evidence and combining it with generative reasoning. This review describes the basic structure of RAG with the focus on retrieval pipelines, embedding-based indexing, reranking strategies, and knowledge fusion processes. It also shows how techniques like hybrid dense-sparse retrieval, graph-based knowledge modeling and adaptive query reformulation reinforce retrieval accuracy and reasoning power. An overall overview of the latest literature shows the increase in the sophistication and variety of RAG implementations, including modular architectures and more refined domain models, as well as scalable enterprise-ready systems. The empirical evidence continuously demonstrates that RAG performs better than standalone language models in tasks that involve grounded reasoning, contextual fidelity, and evidence-based response, providing it with a key paradigm of the next generation of intelligent systems.

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Keywords:

Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), Semantic Search, Hybrid Retrieval, Text Generation, Knowledge-Driven NLP, Question Answering, RAFT, Dense and Sparse Indexing, Natural Language Processing.