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How Larry uses a surgical edit loop to keep AI-written legal articles accurate

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A development team built Larry, an AI writing agent designed to turn lawyers' expertise into citable legal articles for platforms like Google and ChatGPT. Instead of regenerating entire articles on each edit, Larry uses a surgical replace_in_field tool that patches only the specific sentence or section needing a change, preserving the expert's voice and preventing factual drift. The agent follows a structured interview-first workflow, asking the lawyer two to three targeted questions and requiring a real anonymized case before any writing begins. Built on Next.js, OpenRouter, Supabase, and Tavily, the system runs as a stateful, framework-free while loop that persists every conversation turn and tool call. The team argues the core value of any expert-facing AI product lies not in prose generation but in capturing domain judgment that a one-shot prompt cannot replicate.

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