User Experience Patterns for Front-End Integration of Retrieval-Augmented Generation in Enterprise Platforms
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V6I2P111Keywords:
RAG UI patterns, front-end integration, enterprise AI interfaces, intelligent retrieval, user experience designAbstract
The concept of Retrieval-Augmented Generation (RAG) has become a baseline architecture paradigm of enterprise-level artificial intelligence systems that allow large language models (LLMs) to generate accurate, context-aware and verifiable responses by basing generation on external sources of knowledge. Although there are studies dedicated to the issues of back-end architecture, retrieval optimization, and model performance, limited attention is dedicated to user experience (UX) concerns regarding the implementation of RAG systems as a part of enterprise front-end systems. This breach is decisive, with system accuracy and transparency, trust, usability, and alignment of workflow being paramount factors in the adoption of the enterprise. The current paper is the detailed analysis of user experience patterns of front-end integration of Retrieval-Augmented Generation to enterprise platforms. It logically examines model of interaction, interface design, feedback schemes and explanability approaches that affect user confidence and effectiveness. The study integrates human-computer interaction (HRI), explainable artificial intelligence (XAI), and enterprise software design to suggest a structured UX pattern taxonomy to be used in systems with RAG.The methodology it uses is mixed, which incorporates research writing design science, some UX heuristic evaluation, and empirical usability testing involving several enterprise applications such as knowledge management, customer support, and decision support systems. Task completion time, perceived usefulness and trust calibration are measured in quantitative metrics as well as qualitative feedback on the users.The findings prove that properly developed UX patterns (source attribution panels, retrieval confidence indicators, iterative query refinement interfaces) can help the user gain great trust, cognitive, and decision-making accuracy. The paper ends with operational design considerations and future inquiries that innovate UX as an upper-class element in the adoption of a successful implementation of Retrieval-Augmented Generation in business settings
Downloads
References
[1] Lewis, P., Perez, E., Piktus, A., et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Advances in Neural Information Processing Systems (NeurIPS), 33, 9459–9474.
[2] Karpukhin, V., Oguz, B., Min, S., et al. (2020). Dense Passage Retrieval for Open-Domain Question Answering. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 6769–6781.
[3] Viswanathan, Venkatraman. "Embedding Ethical Principles into Generative AI Workflows for Project Teams." (2024).
[4] Izacard, G., & Grave, E. (2021). Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering. Proceedings of the 16th Conference of the European Chapter of the ACL (EACL), 874–880.
[5] Guu, K., Lee, K., Tung, Z., et al. (2020). REALM: Retrieval-Augmented Language Model Pre-Training. Proceedings of the 37th International Conference on Machine Learning (ICML), 3929–3938.
[6] Luan, Y., Eisenstein, J., Toutanova, K., et al. (2021). Sparse, Dense, and Attentional Representations for Text Retrieval. Transactions of the Association for Computational Linguistics, 9, 329–345.
[7] Goyal, Mahesh Kumar, and Rahul Chaturvedi. "Detecting Cloud Misconfigurations with RAG and Intelligent Agents: A Natural Language Understanding Approach." Available at SSRN 5271734 (2025).
[8] Hearst, M. A. (2009). Search User Interfaces. Cambridge University Press.
[9] Amershi, S., Weld, D., Vorvoreanu, M., et al. (2019). Guidelines for Human-AI Interaction. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 1–13.
[10] Shneiderman, B. (2020). Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy. International Journal of Human–Computer Interaction, 36(6), 495–504.
[11] Bansal, G., Nushi, B., Kamar, E., et al. (2019). Beyond Accuracy: The Role of Mental Models in Human-AI Team Performance. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 2(1), 2–11.
[12] Ribeiro, M. T., Singh, S., &Guestrin, C. (2016). “Why Should I Trust You?”: Explaining the Predictions of Any Classifier. Proceedings of the 22nd ACM SIGKDD Conference, 1135–1144.
[13] Doshi-Velez, F., & Kim, B. (2017). Towards a Rigorous Science of Interpretable Machine Learning. arXiv preprint arXiv:1702.08608.
[14] Miller, T. (2019). Explanation in Artificial Intelligence: Insights from the Social Sciences. Artificial Intelligence, 267, 1–38.
[15] Kaur, H., Nori, H., Jenkins, S., et al. (2020). Interpreting Interpretability: Understanding Data Scientists’ Use of Interpretability Tools. Proceedings of the 2020 CHI Conference, 1–14.
[16] Zhang, Y., & Chen, X. (2023). Enhancing Trust in Large Language Models via Retrieval-Augmented Generation. ACM Transactions on Intelligent Systems and Technology, 14(4), 1–22.
[17] Ehsan, U., Harrison, B., Chan, L., &Riedl, M. (2019). Human-Centered Explainable AI: Towards a Reflective Sociotechnical Approach. IEEE Computer, 52(8), 42–52.
