## 1 Introduction  
Tools for effective website content management, performance optimisation, ensuring web accessibility, and keeping data safe and secure are critical in the age of the Internet boom [1, 2]. There has been a breakthrough in this domain in recent years thanks to the expansion of large language models (LLMs), such as the Generative Pre-trained Transformer (GPT). Not only can they process natural languages, but they also support AI plugins, which are sophisticated components of diverse functionalities [3]. The most promising areas of AI plugin development include cross-platform integration and expansion of the tool and algorithm capabilities of real-time third-party data analysis [4]. This means the tools can be expected to transcend their ecosystem and training datasets more easily and interact with the actual online ecosystem and even the digital ecosystem in real time. This has both positive and negative implications, but for website and web application quality audits, there are two new directions of optimisation and improvement [5]. Integrating AI and third-party auditing tools can yield more precise and context-anchored diagnoses of problems in such areas as performance, content accessibility, or content quality. Still, its primary advantage can be better-tailored recommendations [6]. Notably, all the functions are inherently available through a conversational user interface (CUI), so the auditor can ask auxiliary questions about details of the LLM output with prompts. Still, despite the benefits, using LLM-based AI plugins raises questions about their limitations, including those linked to the reliability of the measurements (results) and the recommendations based on them. The concerns are connected with such areas as AI hallucinations, which have already been diagnosed [7]. Another disconcerting matter is the reported paucity of new training data [8]. All these factors call for a cautious and critical approach to using LLMs [9]. Search engine optimisation (SEO) is among the primary fields of use of LLMs in quality testing. This is because LLMs can streamline content optimisation through auditing, text generation, meta tag, and header structure analysis and selection of keywords [10]. Still, these studies focus on a natural LLM environment, which is the website text (front-end). Another area often audited with LLMs is web accessibility [5]. The studies are comparative: results generated by AI are verified with traditional measurement tools. They have revealed that LLMs are not proficient at evaluating the quality of specialised functions and non-standard attributes, which may lead to incomplete or erroneous audit (design) recommendations. LLMs are also employed in user interface tests [11]. Here, too, many of their recommendations are projections and estimations. Therefore, LLMs fall short of traditional measurement and development tools. Similar conclusions are offered from research on LLM performance in web application security audits [1] and website performance measurement and optimisation [12]. The authors demonstrated that although AI could improve certain processes, it still exhibited significant limitations compared to dedicated tools. Moreover, the researchers faced the problem of AI hallucinations in most cases. They were the most conspicuous for dynamic phenomena changing in real time. It was also hard to verify AI results. LLM's propensity for hallucinations is particularly challenging when it can lead to wrong audit recommendations. Moreover, researchers today often overlook the issue of the combination of the model's general knowledge (trained with big data) and task-specific knowledge, which may result in lower accuracy of results offered by AI [13; 14]. The present study follows a more adaptive approach, integrating the model's general knowledge with task-focused (contextual) knowledge to improve website quality audit reliability. It involves AI plugins dedicated to website and web application quality auditing and attempts to test their performance in real-life conditions.