{"id":14401,"name":"VerityAI","purpose":"A tool for developers to systematically identify and mitigate 'negation neglect' in LLM outputs by providing explicit, tunable 'doubt factors' for model responses based on historical accuracy and contextual analysis. It integrates directly into code pipelines to enforce a higher degree of reliability in AI-generated content, especially critical for financial or legal applications.","profitable":1,"date_generated":"Friday May 2026 01:08","reference":"verityai-negation-mitigation","technology_advise":["Python","PostgreSQL","Medium"],"development_time_estimation_mvp_in_hours":180,"grade":7.8,"category":"devtools","view_count":21,"similar_ideas":[{"id":14408,"name":"VerityAI","grade":7.8,"category":"ai"},{"id":14405,"name":"Verity AI Shield","grade":8.2,"category":"ai"},{"id":12862,"name":"Verity AI","grade":7.5,"category":"ai"},{"id":4212,"name":"VerityCheck AI","grade":8.2,"category":null},{"id":8298,"name":"VerityAI Authenticator","grade":8.2,"category":"ai"}],"source_headline":"LLMs believe false statements even after explicit warnings that they're false"}