AI in Web Development: A Comparative Study of Traditional Coding and LLM-Based Low-Code Platforms

Abstract

Web development supports business, education, and public services online, so speed and reliability are important. Low-code and no-code (LCNC) platforms aim to save time by using visual tools instead of writing all code. The impact of these platforms when combined with large language models (LLMs) has not been well studied. This paper compares a chatbot built in three coding stacks (Node.js, Python, Ruby) and one LCNC workflow in n8n that uses LLMs (Grok, Gemini, ChatGPT). The same tasks and prompts were used to test development time, speed, user ratings, and answer quality (precision, recall, F1). The study shows that LCNC with LLMs reduced build time by about 60 percent while keeping response speed close to hand-coded systems and reaching high answer quality (F1 up to 90 percent) with strong user approval. To clarify the main objective, the paper aims to evaluate whether LCNC+LLM integration offers a practical alternative to traditional coding approaches for intelligent web applications, particularly in terms of efficiency and maintainability. The challenge addressed is the limited empirical evidence comparing these two paradigms under identical conditions and using consistent performance metrics. Results are also interpreted relative to competing approaches in conventional development workflows, highlighting where LCNC tools match, exceed, or fall behind manual coding. Some areas, such as security and error handling, still require extra care and represent limitations of the present study. Overall, results show that LCNC with LLMs can be a useful way to build fast and reliable tools while lowering the development barrier for both developers and non-developers.