Retrieval-Augmented Generation for Large Language Model-Based Intelligent Assistants: A Review

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Zilang Shao
School of Artificial Intelligence, Shenyang University of Technology, Shenyang, China

DOI: https://doi.org/10.70267/aitia.2026482489

Keywords

retrieval-augmented generation, large language models, intelligent assistants, knowledge augmentation, hallucination mitigation, information retrieval, trustworthy AI

Abstract

Large language models have accelerated the development of intelligent assistants by providing flexible natural-language understanding and generation. However, hallucination, knowledge staleness, and limited coverage of domain-specific information continue to restrict their reliability in knowledge-intensive tasks. This review examines how Retrieval-Augmented Generation (RAG) can strengthen LLM-based intelligent assistants by connecting generative capability with external, maintainable knowledge. It synthesizes research on the technical foundations of RAG, key components and optimization strategies, and applications and challenges in intelligent-assistant settings. The review finds that RAG can improve knowledge accuracy and timeliness by grounding responses in retrieved evidence and allowing knowledge resources to be updated independently of the base model. These benefits are conditional: unreliable retrieval, poorly maintained sources, ineffective use of context, and fragmented evaluation can still produce unsupported or unsafe answers. Reliable deployment, therefore, requires coordinated retrieval quality, knowledge management, generation control, and trustworthy evaluation. Future RAG-based assistants should combine these capabilities to become scalable, secure, evidence-aware, and verifiable systems.

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References

  • [1] T. Brown et al., ‘Language Models are Few-Shot Learners’, Advances in Neural Information Processing Systems, vol. 33, pp. 1877–1901, 2020.
  • [2] W. X. Zhao et al., ‘A Survey of Large Language Models’, Front. Comput. Sci., vol. 20, no. 12, p. 2012627, May 2026, doi: 10.1007/s11704-026-60308-3.
  • [3] L. Huang et al., ‘A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions’, ACM Trans. Inf. Syst., vol. 43, no. 2, p. 42:1 -42:55, Jan. 2025, doi: 10.1145/3703155.
  • [4] Y. Zhang et al., ‘Siren’s Song in the AI Ocean: A Survey on Hallucination in Large Language Models’, Computational Linguistics, vol. 51, no. 4, pp. 1373–1418, Dec. 2025, doi: 10.1162/COLI.a.16.
  • [5] A. Kostikova, Z. Wang, D. Bajri, O. Pütz, B. Paaßen, and S. Eger, ‘LLLMs: A Data-Driven Survey of Evolving Research on Limitations of Large Language Models’, ACM Comput. Surv., vol. 58, no. 11, p. 282:1-282:33, Apr. 2026, doi: 10.1145/3801096.
  • [6] P. Lewis et al., ‘Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks’, in Advances in Neural Information Processing Systems, Curran Associates, Inc., 2020, pp. 9459–9474. Accessed: Jul. 14, 2026. [Online]. Available: https://proceedings.neurips.cc/paper/2020/hash/6b493230205f780e1bc26945df7481e5-Abstract.html
  • [7] Y. Huang and J. X. Huang, ‘ A Survey on Retrieval-Augmented Text Generation for Large Language Models’, ACM Comput. Surv., vol. 58, no. 12, p. 300:1-300:38, May 2026, doi: 10.1145/3805774.
  • [8] C. Sharma, ‘Retrieval-Augmented Generation: A Comprehensive Survey of Architectures, Enhancements, and Robustness Frontiers’, May 28, 2025, arXiv: arXiv:2506.00054. doi: 10.48550/arXiv.2506.00054.
  • [9] A. Brown, M. Roman, and B. Devereux, ‘ A Systematic Literature Review of Retrieval-Augmented Generation: Techniques, Metrics, and Challenges’, Sep. 09, 2025, arXiv: arXiv:2508.06401. doi: 10.48550/arXiv.2508.06401.
  • [10] Y. Gao et al., ‘Retrieval-Augmented Generation for Large Language Models: A Survey’, Mar. 27, 2024, arXiv: arXiv:2312.10997. doi: 10.48550/arXiv.2312.10997.
  • [11] Y. Li, X. Fu, G. Verma, P. Buitelaar, and M. Liu, ‘Mitigating Hallucination in Large Language Models (LLMs): An Application-Oriented Survey on RAG, Reasoning, and Agentic Systems’, Oct. 28, 2025, arXiv: arXiv:2510.24476. doi: 10.48550/arXiv.2510.24476.
  • [12] G. Izacard et al., ‘Unsupervised Dense Information Retrieval with Contrastive Learning’, Aug. 29, 2022, arXiv: arXiv:2112.09118. doi: 10.48550/arXiv.2112.09118.
  • [13] V. Karpukhin et al., ‘Dense Passage Retrieval for Open-Domain Question Answering’, in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), B. Webber, T. Cohn, Y. He, and Y. Liu, Eds, Online: Association for Computational Linguistics, Nov. 2020, pp. 6769– 6781. doi: 10.18653/v1/2020.emnlp-main.550.
  • [14] M. Cheng et al., ‘A Survey on Knowledge-Oriented Retrieval-Augmented Generation’, Mar. 17, 2025, arXiv: arXiv:2503.10677. doi: 10.48550/arXiv.2503.10677.
  • [15] D. Edge et al., ‘From Local to Global: A Graph RAG Approach to Query-Focused Summarization’, Feb. 19, 2025, arXiv: arXiv:2404.16130. doi: 10.48550/arXiv.2404.16130.
  • [16] P. Liu, W. Yuan, J. Fu, Z. Jiang, H. Hayashi, and G. Neubig, ‘Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing’, ACM Comput. Surv., vol. 55, no. 9, p. 195:1-195:35, Jan. 2023, doi: 10.1145/3560815.
  • [17] S. Verma, ‘Contextual Compression in Retrieval-Augmented Generation for Large Language Models: A Survey’, Oct. 02, 2024, arXiv: arXiv:2409.13385. doi: 10.48550/arXiv.2409.13385.
  • [18] N. F. Liu et al., ‘Lost in the Middle: How Language Models Use Long Contexts’, Transactions of the Association for Computational Linguistics, vol. 12, pp. 157–173, 2024, doi: 10.1162/tacl_a_00638.
  • [19] S.-Q. Yan, J.-C. Gu, Y. Zhu, and Z. -H. Ling, ‘Corrective Retrieval Augmented Generation’, Oct. 2024, Accessed: Jul. 15, 2026. [Online]. Available: https://openreview.net/forum?id=JnWJbrnaUE
  • [20] A. Asai, Z. Wu, Y. Wang, A. Sil, and H. Hajishirzi, ‘Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection’, International Conference on Learning Representations, vol. 2024, pp. 9112–9141, May 2024.
  • [21] Y. Zhou, Z. Liu, and Z. Dou, ‘AssistRAG: Boosting the Potential of Large Language Models with an Intelligent Information Assistant’, Nov. 11, 2024, arXiv: arXiv:2411.06805. doi: 10.48550/arXiv.2411.06805.
  • [22] H. Yu, A. Gan, K. Zhang, S. Tong, Q. Liu, and Z. Liu, ‘Evaluation of Retrieval-Augmented Generation: A Survey’, in Big Data, W. Zhu, H. Xiong, X. Cheng, L. Cui, Z. Dou, J. Dong, S. Pang, L. Wang, L. Kong, and Z. Chen, Eds, Singapore: Springer Nature, 2025, pp. 102–120. doi: 10.1007/978-981-96-1024- 2_8.
  • [23] B. Palanisamy, G. S. S. Chalapathi, V. Hassija, and R. Buyya, ‘Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems’, Jun. 24, 2026, arXiv: arXiv:2606.25533. doi: 10.48550/arXiv.2606.25533.
  • [24] A. Singh, A. Ehtesham, S. Kumar, T. T. Khoei, and A. V. Vasilakos, ‘Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG’, Apr. 01, 2026, arXiv: arXiv:2501.09136. doi: 10.48550/arXiv.2501.09136.

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Published
Sep 13, 2026
Conference Proceedings Volume
Vol. 16 (2026): Proceedings of the 2026 International Conference on AI and Technological Innovation Applications (AITIA 2026)
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Articles
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How to Cite

Shao, Zilang. “Retrieval-Augmented Generation for Large Language Model-Based Intelligent Assistants: A Review”. Exploring Science Academic Conference Series, vol. 16, Sept. 2026, pp. 482-9, https://doi.org/10.70267/aitia.2026482489.