Publication Title
Journal of Law & Empirical Analysis
Volume
3
Page
220
Year
2026
Abstract
Generative AI is set to transform the legal profession, though its most promising uses and ultimate effects are still unclear. While AI models like GPT-4 improve efficiency, they can also “hallucinate” and may undermine legal judgment, particularly in complex tasks typically handled by skilled lawyers. This article examines two emerging AI innovations that may mitigate these concerns: Retrieval Augmented Generation (RAG), which grounds AI-powered analysis in legal sources, and AI reasoning models, which structure complex reasoning before generating output. We conduct the first randomized controlled trial assessing these technologies, assigning upper-level law students to complete legal tasks using a RAG-powered legal AI tool (Vincent AI, 2024), an AI reasoning model (OpenAI’s o1-preview), or no AI. We find that both AI tools significantly enhance legal work quality, a marked contrast with previous research examining older large language models like GPT-4. Moreover, these newer models appear to maintain the efficiency benefits associated with older AI technologies. Our findings also show that these AI tools significantly boost productivity in five out of six tested legal tasks, with statistically significant gains of anywhere from 50% to 130%. They perform exceptionally well in complex tasks like drafting persuasive letters and analyzing complaints. Notably, o1-preview improves the analytical depth of work product and Vincent AI avoids introducing more hallucinations, suggesting that integrating domain-specific RAG capabilities with reasoning models could yield even larger improvements.
Recommended Citation
Daniel Benjamin Schwarcz, Sam Manning, J.J. Prescott, Patrick Barry, David R. Cleveland, and Beverly Rich, AI-Powered Lawyering: AI Reasoning Models, Retrieval Augmented Generation, and the Future of Legal Practice, 3 J. L. & Empirical Analysis 220 (2026), available at https://scholarship.law.umn.edu/faculty_articles/1207.
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Archival Statement
Note: The documents on this page were created before current policy requirements took effect, and therefore may not be accessible. Request this content in an accessible format.
