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Can Reasoning Models Boost Productivity in Legal Practice by up to 140%?

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Traditional legal practice, which includes reviewing documents by hand, doing thorough research, and writing contracts that take a lot of time, is quickly changing with the integration of AI. Legal AI solutions promise to be more efficient, less expensive, and more accurate, but they also raise questions about regulatory compliance, hallucinations undermining legal judgment and more.

Recently, researchers from the University of Minnesota Law School, University of Michigan Law School, Centre for the Governance of AI, and Ogletree Deakins conducted the first randomised controlled study evaluating students’ performance using RAG-based legal AI tool, an AI reasoning model, and without AI. The results showed that reasoning models have made a breakthrough in intelligent document processing and productivity of legal practice.

We talked to Sergii Bataiev, Director of Architecture and Technology at ELEKS, to learn more about these results and what they mean.

What are the main highlights from the research you would like to pinpoint?

So, the experiment involved using Vincent AI as an AI tool and o1-preview OpenAI for a reasoning model. Vincent AI provided a productivity increase of approximately 38% to 115%, while o1-preview increased productivity from 34% to 140%, with particularly strong effects in complex tasks such as drafting persuasive legal letters and analysing legal complaints.

What's even more important is that the number of hallucinations proved to be extremely small. And for Vincent AI, it was approximately the same as for law students who didn't use AI at all.

Which areas of legal practice are most ripe for transformation by reasoning models?

The study suggests reasoning models can significantly transform litigation-oriented practice areas and complex legal analysis, where they demonstrated concentrated quality improvements and highly accurate exam feedback when given proper context. In contrast, transactional work, specifically contract drafting, showed minimal improvement with AI assistance.

What are the key takeaways of this research?

First, the study results clearly showed that combining domain-specific RAG capabilities with reasoning models improves legal knowledge and work efficiency when working together. Second, these results mean that the next wave of AI-based legal tools is almost here, which will completely change the legal field.

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