THE ROLE OF RAG IN TRANSFORMING LEGAL INFORMATION RETRIEVAL AND AUTOMATED LEGAL CONSULTATION
Keywords:
artificial intelligence, legal query processing, search-augmented generation (RAG), legal information retrieval, explainable AI, legal data engineering, semantic search, automated legal reasoning.Abstract
The ever-increasing volume and complexity of legal information require intelligent systems capable of processing legal queries with high accuracy. Traditional search methods often fail to identify the correct legal norms or provide context-based interpretations. This paper explores how modern artificial intelligence techniques, in particular, Augmented Generation through Search (AGS), can improve legal information retrieval and automated legal reasoning. By combining vector-based search of authoritative laws with generative models, AGS reduces hallucinations and ensures that answers remain based on proven legal sources. The study describes the system workflow, the challenges of data preparation, and the importance of maintaining a modern legal corpus. The results show that AGS-based approaches significantly improve the reliability, comprehensibility, and relevance of AI-generated legal answers, which is a significant breakthrough in the field of digital legal assistance.
References
R. Islam, H. Alabsi, “Artificial Intelligence in Legal Research: Opportunities and Challenges,” Journal of Law & Technology, vol. 12, no. 2, pp. 45–63, 2021.
P. Lewis et al., “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks,” Advances in Neural Information Processing Systems (NeurIPS), 2020.
C. C. Aggarwal, Recommender Systems: The Textbook. Springer, 2016.
F. Ricci, L. Rokach, B. Shapira (eds.), Recommender Systems Handbook. Springer, 2015.
Y. Koren, R. Bell, C. Volinsky, “Matrix Factorization Techniques for Recommender Systems,” Computer, vol. 42, no. 8, pp. 30–37, 2009.
X. He et al., “Neural Collaborative Filtering,” WWW '17: Proceedings of the 26th International Conference on World Wide Web, pp. 173–182, 2017.
F. Sun et al., “BERT4Rec: Sequential Recommendation with Bidirectional Transformer Encoder,” IJCAI, pp. 144–150, 2019..
Published
Issue
Section
License
Copyright (c) 2025 S.Sh. Kobilov, A.I. Goyibnazarov, Q.T.Umurzoqov
This work is licensed under a Creative Commons Attribution 4.0 International License.
(+998)90 142-39-38
m.z.abdutolibov@gmail.com
@universal_jurnal
House 189 Kashgar Street, Marhamat district, Andijan region