Developer's Multi-AI Legal System Passed Internal Tests but Failed on Real Case Data

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A developer building an AI-assisted legal document system found that passing multiple internal AI reviews did not guarantee real-world reliability, after the system failed critically when tested on an actual legal case. The system confused parties, misread monetary figures, mixed opposing claims, and omitted key information — errors none of the AI reviewers had flagged. This prompted the developer to formalize what they call the CHIMERA SYSTEM, a human-directed workflow that assigns distinct roles — architecture, execution, adversarial review, and validation — to different AI models. Google Drive serves as shared memory and a decision log across models, keeping the full reasoning trail visible to the human operator. The core takeaway is that consensus among multiple AI models is not a substitute for real-world testing and human judgment.

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