Navigating the world of AI for legal research can be complex. But what if you could confidently test and evaluate the AI tools designed for lawyers, just like an expert?
In a market flooded with options, understanding how to benchmark AI tools to demonstrate their return on investment in a relevant way for your business is crucial.
At Thomson Reuters, we’ve invested countless hours in AI testing for legal research tools and we’ve learned a lot. Many common testing methods for AI tools are inherently flawed, from overly emphasising initial answer accuracy and using unrealistic questions to failing to calculate confidence intervals properly.
To understand if an AI tool is worth your time, you need to follow a set of clear, repeatable steps that align with how you’d actually use the tool in your day-to-day work, focusing on what you really need from it.
Inside the guide: Best practices for benchmarking AI for legal research
Our guide, Best Practices for Benchmarking AI for Legal Research, outlines an effective benchmarking methodology and prepares you to evaluate future AI technologies in legal research.
Mike Dahn, Head of Westlaw Product, and Dasha Herrmannova, Senior Applied Scientist, from Thomson Reuters have pulled together 11 best-practice tips for testing AI tools, based on Thomson Reuters’ decades of experience, including:
- The two most critical metrics to prioritise (hint: initial answer accuracy isn’t one of them)
- Why you need a combination of automated and manual evaluation
- The importance of representative, realistic test questions
- How to ensure your results are statistically valid
Making the most of AI in legal research
AI isn’t just about saving time. The right tools can help you dig deeper, argue better, and offer more to your clients. But the market is crowded, and not all tools are up to the mark. Our guide will help you identify the good and the bad. Download your copy via the form on this page.
