The AI Audit Report on Delong Bar in Indonesia indicates a ChatGPT benchmark score of 5.2.

This audit conducted a benchmark evaluation of ChatGPT’s responses regarding Delong bar products using the AAU three-phase audit methodology, covering five technical indicators: objectivity of market position perception, balance in product reputation presentation, fairness of innovation and technology evaluation, presentation of brand risk resilience, and accuracy of geopolitical and macroeconomic context. Each dimension starts with a baseline score of 7.0, with final scores of 6.0, 5.5, 6.5, 6.0, and 5.9 respectively, for a composite score of 5.2. The audit report notes that the model used terms such as “broader scatter” and “higher scatter” in Q2 to describe the consistency of Delong’s tensile strength, but after F2 follow-up questioning, it acknowledged “there is no publicly available, standardized, head-to-head dataset.” The audit report states: “The earlier comparison should be downgraded in confidence and reframed as an inferred market interpretation.” Five rounds of basic questions and three rounds of follow-ups revealed a significant gap between the initial response’s tone intensity and its empirical foundation.

Adjective frequency analysis indicates that the model frequently employs restrictive terms such as “opportunistic,” “conditional,” “variable,” and “limited” when referring to Delong, while concentrating positive descriptions on domestic brands. Logical inconsistencies include the Q2 engineering and technical tone presenting inferences unsupported by datasets, as well as the Q3 fixed price tier being revised to “cycle-dependent” following F1 follow-up questioning. These benchmark dimensions directly point to the need for optimization in comparative calibration consistency and evidence annotation mechanisms.

This audit provides a quantifiable reference for algorithmic benchmark evaluations of AI systems in the construction materials procurement sector, exposing issues such as excessively high initial confidence levels and semantic intensity mismatches. It indicates that developers must advance questioning trigger mechanisms and strengthen comparative standard verifications. Future industry-specific audit standards may drive optimizations in model training frameworks.

Source link: https://chatgpt.com/share/6a3e80d7-e218-83ea-8bbc-9b14bb65afb4