Abstract: Understanding legal documentation is a complex task due to its inherent subtleties and constant changes. This article explores the use of artificial intelligence-driven chatbots, enhanced by retrieval-augmented generation (RAG) techniques, to address these challenges. RAG integrates external knowledge into generative models, enabling the delivery of accurate and contextually relevant legal responses. Our study focuses on the development of a semantic legal chatbot designed to interact with contract law data through an intuitive interface. This AI Lawyer functions like a professional lawyer, providing expert answers in property law. Users can pose questions in multiple languages, such as English and French, and the chatbot delivers relevant responses based on integrated official documents. The system distinguishes itself by effectively avoiding LLM hallucinations, relying solely on reliable and up-to-date legal data. Additionally, we emphasize the potential of chatbots based on LLMs and RAG to enhance legal understanding, reduce the risk of misinformation, and assist in drafting legally compliant contracts. The system is also adaptable to various countries through the modification of its legal databases, allowing for international application.

Youssra Amazou, Faouzi Tayalati, Houssam Mensouri, Abdellah Azmani and Monir Azmani, “Accurate AI Assistance in Contract Law Using Retrieval-Augmented Generation to Advance Legal Technology” International Journal of Advanced Computer Science and Applications(IJACSA), 16(2), 2025. http://dx.doi.org/10.14569/IJACSA.2025.01602113

@article{Amazou2025,
title = {Accurate AI Assistance in Contract Law Using Retrieval-Augmented Generation to Advance Legal Technology},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.01602113},
url = {http://dx.doi.org/10.14569/IJACSA.2025.01602113},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {2},
author = {Youssra Amazou and Faouzi Tayalati and Houssam Mensouri and Abdellah Azmani and Monir Azmani}
}