New SSRN research throws GPT-4 at legal reasoning benchmarks. The model scores well on LegalBench, which tests contract analysis and statutory interpretation. These are hard problems. The model handles them. But they're not the problems lawyers actually face.
Law school gives you closed-universe assignments with tidy answers. Real litigation doesn't work that way. Context windows cap how much text a model can process at once, and that matters when you're wrestling with hundred-page contracts or boxes of case files. Researchers use Chain-of-Thought prompting to force AI through structured reasoning, mimicking the IRAC method every first-year law student learns. They pair it with Retrieval-Augmented Generation to pull from actual legal databases instead of making up citations that sound right but don't exist.
The paper's methodology has issues too. Evaluators who know they might be grading AI output aren't exactly neutral judges. And benchmarks test reasoning in a vacuum. Real legal work means deadlines you didn't choose, procedural rules you can't bend, and opposing counsel working against you. GPT-4 can reason through a legal problem. That doesn't mean it can practice law. Real world agents have constraints you didn't choose, procedural rules you can't bend, and opposing counsel working against you.