LawGPT: An Intelligent Legal Assistance and Automation Platform utilizing Retrieval-Augmented Generation and Large Language Models
The legal domain is notorious for its complexity which requires extensive document review and case analysis work and research activities to function properly. The traditional approach to legal services creates a barrier that prevents ordinary people from accessing legal help while making legal services too expensive for professionals to use. The intelligent web-based legal assistance platform LawGPT enables people to access legal information through advanced Artificial Intelligence technology. LawGPT uses a Retrieval-Augmented Generation (RAG) architecture to handle hallucination risks which standard Large Language Models (LLMs) face. The sys
Resource information
- Authors
- Jagadeesh D Pujari, Varsha S Jadhav, Prerana Shisanalli, Rahul Dhagati, Siddaram Soorgond, Sunil R Rathod, Bhumika C. Sajjan, Vivek Choudhary
- Research area
- Artificial intelligence
- Research topics
- Retrieval-Augmented Generation, Large Language Models, Artificial Intelligence, semantic search, legal research
- Publication date
- 2026
- DOI
- 10.5281/zenodo.20026003
- Upstream last modified
- Aug 29, 2026
- License
- Creative Commons Attribution Non Commercial 4.0 International
- Last record update
- Nov 6, 2026
About this resource
The legal domain is notorious for its complexity which requires extensive document review and case analysis work and research activities to function properly. The traditional approach to legal services creates a barrier that prevents ordinary people from accessing legal help while making legal services too expensive for professionals to use. The intelligent web-based legal assistance platform LawGPT enables people to access legal information through advanced Artificial Intelligence technology. LawGPT uses a Retrieval-Augmented Generation (RAG) architecture to handle hallucination risks which standard Large Language Models (LLMs) face. The system uses transformer-based embedding models to process legal data which includes case laws and contracts and statutes to extract their semantic meaning. The platform uses a structured FAISS vector database to find the most relevant legal context which it retrieves when users submit queries. LangChain dynamically injects context into an LLM to produce context-aware responses which users can highly cite and which maintain accurate information. The platform evaluates experimental legal advisory synthesis through its ability to deliver complex legal advisory materials with high speed and accuracy which makes it a suitable automated solution.