## 5 Discussion  
Effective audit tools are indispensable in light of the growing bulk of online content, the number of Internet users, and the complexity of websites today [62]. LLM-based AI plugins (GPT models) may be an answer to the challenge of improving website and web application quality by equipping testers and auditors with new evaluation and optimisation capabilities. In addition, AI plugins can potentially change how experts approach website monitoring and quality improvement [5]. Nevertheless, the present study has revealed that the results offered by these tools are controversial. First, the test mechanisms are unclear. The issue is highly relevant considering the substantial differences between performance and content presentation on desktop and mobile devices, for example. The exact test configuration, such as the test server location, is also unknown. Second, there is no guarantee that the tests are conducted at all despite the plugins' declarations. During the experimental audits, the AI algorithm reported that the results did not come from actual measurements but were mere illustrations when asked about the test's desktop or mobile configuration. This confession casts a shadow of doubt on the truth value of the figures provided by the AI. The audit results proposed by the AI plugins are limited, and their audit recommendations include conducting an audit with dedicated third-party tools. Note also that the AI audit report is unspecific, which makes it of little use for optimising specific attributes of the website. Moreover, the results are so general that they could be applied to any website. This causes scepticism regarding their completeness and truth value. When asked about the audit method, the LLM admitted it had not conducted an actual analysis because it was incapable of browsing websites and using expert audit tools itself. This further asserts that the reported results were mere illustrations instead of outcomes of actual measurements. The study shows that the auditor (tester) should be particularly cautious when using LLMs, especially when tasking them with such technical quality assessments as performance, accessibility, or SEO metric audits. These attributes fluctuate and need to be measured in real time, often at specific moments [15,51]. Dedicated audit tools outperform AI plugins regarding measurement precision and reliability because they have been designed for specific technical processes. Such tools as WAVE, W3C Markup Validation Service, and Free Broken Link Checker analyse websites and web applications directly. They have crawlers that explore website content and report WCAG errors or HTML/CSS code compliance with W3C [6,50]. Their results are presented as graphics and tables in a clear and unambiguous way, facilitating interpretation, although they, too, have to be verified [15,51]. In the case of AI plugins, it is hard to tell what results from actual measurements and what is a mere projection. During the conversation, the LLM assured the user it was capable of performing actual quality audits in a specific mode and using specific tools. It plainly declared 'the actual test has been performed', and the results came from real-time measurements. After several questions about the technical details, it admitted the figures were not real results but a forecast of what could be achieved with tools dedicated to quality audits. This pattern was recurrent (loop process), which confirms that despite the assertions and declarations by the LLM, the test AI plugins are unable to conduct a real-time audit and do not perform it. Therefore, the results reported by the AI can be classified as hallucinations. In the AI context, the term means that the generated information seems reliable but is actually not based on facts [46]. Alarmingly, if detailed questions are not asked, the LLM asserts that the results are true and accurate. It means that an inexperienced auditor may believe them to be true and include them in their audit report.