  Large Language Models (LLM) have taken the front seat in most of the news
since November 2022, when ChatGPT was introduced. After more than one year, one
of the major reasons companies are resistant to adopting them is the limited
confidence they have in the trustworthiness of those systems. In a study by
(Baymard, 2023), ChatGPT-4 showed an 80.1% false-positive error rate in
identifying usability issues on websites. A Jan. '24 study by JAMA Pediatrics
found that ChatGPT has an accuracy rate of 17% percent when diagnosing
pediatric medical cases (Barile et al., 2024). But then, what is "trust"? Trust
is a relative, subject condition that can change based on culture, domain,
individuals. And then, given a domain, how can the trustworthiness of a system
be measured? In this paper, I present a systematic approach to measure
trustworthiness based on a predefined ground truth, represented as a knowledge
graph of the domain. The approach is a process with humans in the loop to
validate the representation of the domain and to fine-tune the system.
  Measuring the trustworthiness would be essential for all the entities
operating in critical environments, such as healthcare, defense, finance, but
it would be very relevant for all the users of LLMs.
