Abstract
Generative AI systems are increasingly deployed in environments where users rely on multiple responses across sessions, prompts, or conversational turns to inform decisions. While individual outputs may appear internally coherent, large language models can produce subtle or overt contradictions when queried repeatedly on related topics, especially under context variation or prompt rephrasing. These cross-response inconsistencies can degrade user trust, propagate analytical errors, and introduce governance risks in enterprise AI deployments. Traditional validation approaches typically evaluate single responses in isolation and therefore fail to capture this emerging class of reliability failure. This disclosure introduces an AI Response Consistency Checker, a supervisory framework designed to detect semantic and factual contradictions across multiple AI-generated outputs. The system constructs normalized response representations, performs cross-output alignment analysis, and computes a bounded Consistency Risk Score (CRS) representing the likelihood of material contradiction. The architecture is model-agnostic and suitable for real-time or batch deployment in enterprise copilots, knowledge assistants, and automated reporting systems. By identifying cross-response drift early, the framework enables organizations to maintain longitudinal reliability and strengthen trust calibration in AI assisted workflows.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Bhatnagar, Pranav Mr, "AI Response Consistency Checker Detecting Cross-Response Contradictions in Generative Systems", Technical Disclosure Commons, (February 25, 2026)
https://www.tdcommons.org/dpubs_series/9393