Lawyer Led Evaluation of Legal AI

Legal AI accuracy is really a content operations problem disguised as a model problem. In practice, the hard part is not picking the model, it is getting experienced lawyers to write gold standard answers, define what a correct citation looks like, and run repeat tests until the system behaves reliably across drafting, research, review, and due diligence. That is why legal teams treat expert evaluation as most of the implementation work.

• The workflow is concrete. Legal experts create benchmark answers, teams lower model temperature, then score outputs for answer quality, accuracy, hallucination rate, citation quality, and consistency. In this interview, that expert led loop accounts for about 70 percent of the effort.
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• This is also why products separate by workflow. Harvey is strongest in reasoning and drafting, Legora in structured team workflows, and Luminance in more deterministic document review and due diligence. Each category needs different ground truth and review criteria from legal specialists.
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• The closer a tool gets to repeatable legal work, the more domain tuning matters. Spellbook wins narrower contract tasks by building rails inside Word, while broader platforms try to cover research, drafting, and review together. Trust comes from owning a specific workflow and proving accuracy there first.
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The next phase of legal AI will be won by vendors that turn lawyer judgment into a repeatable evaluation system, then plug that system into daily workflows and system integrations. As products move from copilot to agent, the scarce asset will be proprietary legal feedback loops, not raw model access.