verification methods

Effective verification of AI-generated content requires independent, deterministic checks that do not rely on the same model that produced the output, since LLMs confidently fabricate plausible-but-false quotes and citations even when prompted to self-verify, with measured failure rates near 29% in tested samples. Failures frequently originate in the presentation layer rather than retrieval, meaning corrupted synthesis can occur on top of correctly stored source material, a distinction that determines whether to patch the renderer or re-ingest the underlying corpus. The generalizable principle is architectural separation between generator and verifier: sharing model architecture reproduces identical blind spots, while a mechanical, independently-written re-check is the only reliable way to catch fabrication the model cannot see