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67 I L T A N E T . O R G A little over a year ago, the legal industry started talking about generative AI (Gen AI) and its never-ending possibilities for the legal profession. In a remarkably short time, that vision became a reality, especially regarding e-discovery. However, this has created a whole new set of challenges for practitioners. How do they evaluate the defensibility of these solutions? How do they know they can trust the results? How do they stay on top of the ever-changing technological advances? How do they protect their data and their client's data? The answers are not simple. This article will identify a set of best practices and discovery questions that can be used to evaluate Gen AI e-discovery solutions for legal professionals. These best practices and questions were compiled from an analysis of Relativity's Gen AI-powered e-discovery tool, aiR for Review. What is the tool's intended purpose, and which model(s) does it rely upon? Understanding the use cases the solution is built for and what it claims to do is essential. Is it a replacement for first-pass review, a means for better QC, a way to issue code documents, a summarization tool, etc.? Then, evaluate whether it does what it purports to do – request a demonstration and, if possible, obtain access so you can try it out to see firsthand whether it genuinely works. If you are considering multiple technologies, be careful not to compare a solution whose primary purpose is summarization to a solution whose main purpose is for first-pass review – that will not be a fair comparison. Many Gen AI models are in the marketplace, each with different strengths and weaknesses. Find out which model or models are used by the product. Some products use more than one model, depending on the task. A company should be able to tell you which model is used for which task, following a core Gen AI principle of transparency. For example, aiR for Review currently uses GPT-4 for document analysis. GPT- 4 scored in the top 10% on the Uniform Bar Exam and is highly performant. Other solutions may be using GPT-3.5, which failed the Uniform Bar Exam and may not yield as performant results, depending on the task. How does the product keep data private and secure? Data privacy and security should be at the forefront of any technological solution, whether it incorporates Gen AI or not. You need to know where your client's data is going, who has access to it, and whether it is being stored or retained by another party. Gen AI includes creating a series of prompts where you ask the model a question or make a classification decision, and then it returns an output. In e-discovery use cases, the data itself may be sent to the model, along with the instructions. It is essential to understand where the model is housed and who has access to the data going through it. It is also crucial to I L T A N E T . O R G "You need to know where your client's data is going, who has access to it, and whether it is being stored or retained by another party."

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