## 2 Background
### Related Work
[PERSON] and [PERSON] [10] investigated how LLMs could be applied to e-commerce website optimisation for search engines (SEO). They discussed classic technical SEO and content optimisation problems and outlined the SEO capabilities of ChatGPT and DALL-E. They also proposed a list of prompts for generating content and HTML code useful in e-commerce website optimisation. They then enumerated the benefits and problems of using LLMs for e-commerce SEO. [PERSON] et al. [11] proposed a concept of large user interface models (LUIMs) for generating user interfaces for mobile applications and predicting their usability with AI. Their results have demonstrated that the AI tools they tested underperformed when assessing UI/UX. [PERSON] and [PERSON] [1] analysed the application of LLMs in web application security audits. They investigated front-end applications' vulnerabilities to malware attacks to leak confidential data. Using a sequence of prompts and few-shot prompting, they generated code segments for sensitive data processing and evaluated their security. They then juxtaposed results provided by LLMs with their manual evaluation, demonstrating that LLMs have a significant potential to automate analytical tasks. [PERSON] [38] pointed out the problem with AI hallucinations. In the AI domain, hallucinations are language models' responses at variance with reality, which the literature equals to lies, fabrications, confabulations, or even forgery [38; 44]. Put simply, AI hallucinations are caused by LLM's limited ability to process data and lack of full understanding of the context of the question. For instance, an LLM can generate a'made-up' answer when it cannot access relevant information or when the reply is statistically probable [45]. The literature shows that despite the ongoing efforts to improve AI, it is still prone to halluc \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **Scope of LLM Application to Improve Website Quality** & **Audited Aspects and Quality Attributes** & **Reference** \\ \hline
1. & Effectiveness of detecting issues with web accessibility. & Compliance with Web Content Accessibility Guidelines & [2; 6; 40] \\  & Comparison of LLM results with outcomes of manual tools. & (W3C WCAGs). & \\ \hline
2. & Analysis of UGC and extraction of information from large datasets. Comparison of the performance of selected AI & Content audit. Analysis of the semantics and sentiments of UGC. & [39] \\  & tools and manual verification of the output. & & \\ \hline
3. & Automated generation of high-quality websites based on specifications provided in a natural language. & Compliance with HTML and CSS code generated based on user prompts with W3C guidelines. & [5] \\ \hline
4. & Source code inspection for vulnerability to malware-induced data leakage. & Identification of front-end application code segments handling sensitive data and classification of their security levels. & [1] \\ \hline
5. & Audit of the extent to which websites are optimised for search engines (SEO audit). & Analysis of content, meta tags, header structure, and website responsiveness and performance. & [10; 41] \\ \hline
6. & Audit of e-commerce website quality focused on SEO. & Technical SEO audit, keyword analysis, and content audit in the context of e-commerce. & [10; 42] \\ \hline
7. & User interface usability audit; Web Usability Assessment. & Forecast of UX usability in mobile applications using AI. & [11; 43] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Examples of LLM application in website and web application quality audits. cinate, prioritiising the urge to respond to the user even at the expanse of facts [38]. All this amounts to a research gap and the need to test the actual performance of AI plugins dedicated to website quality auditing.