Précis AI Launches Willard to Combat Legislative Hallucinations

General-purpose models like ChatGPT and Claude have infiltrated the policy process, yet their tendency to produce fluent but factually incorrect text presents a growing danger to statutory integrity. The House Office of Legislative Counsel has reportedly struggled with an influx of error-riddled, AI-generated legislation, prompting warnings from legal experts about the risks of automating sensitive federal work.

Stanford’s 2026 AI Index highlights the scale of the problem, documenting hallucination rates as high as 94 percent across leading models. Suprmind’s August tracking confirms that the latest flagships from OpenAI, Anthropic, and Google frequently fail when prompted with unfamiliar queries. Willard attempts to solve this by bypassing broad training sets in favor of a proprietary database of government records. CEO David Fuscus argues that the industry focus on existential AI risks obscures the immediate, tangible damage caused by confident, cited falsehoods. By requiring every response to be tethered to official public records, Willard aims to replace generic inference with archival verification.