Proposed Standard Outlines How Legal AI Products Should Handle Hallucinations

A framework published on DEV Community proposes a 'hallucination defense' standard for legal AI tools, which are expected to produce inaccurate outputs regardless of how advanced they become. The standard identifies four types of hallucinations: citation fabrication, citation drift, factual errors, and flawed reasoning, each requiring distinct technical mitigations. Key defenses include constrained generation, which limits the model to citing only retrieved sources, and citation graph mapping, which links every claim to its canonical legal text. Effective-date tracking is also recommended to prevent practitioners from relying on outdated versions of statutes. The framework is aimed at both legal AI vendors designing their systems and practitioners evaluating which tools to trust.