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[Ed. Note – We have launched The Geek in Review Substack page to put out content in a new way. One example of this content is a series of stories that I’ve been working on as I’ve learned more about how AI and automation tools are developed, and what works, and doesn’t work. As well as the improvements made as foundational models get better, or as the industry learns how to leverage the tools more effectively. Below is Chapter One of my “Beyond the Models” story where I start to dive in on why after two years of some pretty mediocre legal research tools, these tools are suddenly getting much, much better. We’ll still be posting here, but Substack gives us an interesting platform to work with that expands what we can do here on the blog. Including a lot more opportunities for us to hear from you! Come join us and see what all the Substack fuss is about. – Greg]
Beyond the Model: Part One – How Legal AI Got Smart
Preface
Any sufficiently advanced technology is indistinguishable from magic” – Arthur C. Clarke
For those of us in the legal industry, the past three years have created an enthusiasm around the practice and business of law that I’ve never seen before. The introduction of Generative AI created an immediate push into the legal industry, and legal research was seen as the most obvious and easiest candidate to be ‘fixed’ by AI. It has turned out to be one of the hardest.
I’ve spent the last three years trying to keep up. Change isn’t measured in quarterly updates, it is measured in weekly, and sometimes daily increments. Just understanding some of the basics can be challenging. So, I wanted to take a different approach to explaining some of the basics around why legal research seemed like an easy solution, and why it has taken a couple of really awful years of GenAI legal research tools before we started actually seeing some decent results.
Instead of going through all of the data and presenting information in a technical way, I took a page out of my friend Anusia Gillespie’s book and decided to explain it in stories. Storytelling might be a way for some of us to better wrap our heads around what it takes for AI to truly make sense of legal research.
Part one of the story introduces Cooper, Jesse, and Maya. A law firm innovator, a startup data scientist, and a law firm partner. The kind of people who I work with every day. We start off talking about how throwing LLMs at millions of documents of legal decisions doesn’t just work ‘out of the box.’ The near daily news articles of attorneys being sanctioned for “hallucinations” in their legal writings is a direct result of this misconception.
We needed a middle layer to connect the power of the LLM to the legal information, and for a couple of years the answer was Retrieval Augmented Generation (RAG). As you’ll learn from the story, it was a good first step but introduced some of its own problems.
I hope you enjoy this first of what I hope will be many stories of how innovation plays out in the legal field.
(Full article available on Substack)
Introduction
For the past couple of years, lawyers and technologists have marveled at how AI-powered legal research tools seem to be getting smarter every month. Tools like Lexis Protégé, Westlaw CoCounsel, and Vincent by Clio deliver answers that feel almost prescient. The assumption was simple: better foundational models like ChatGPT 5.2, Claude Opus 4.5, and Gemini 3.0 Pro equal better answers.
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And so it begins.
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