Fine-Tuning Large Language Models to generate websites in specific tech stack

Abstract

The rapid advancement of pre-trained Large Language Models (LLMs) has unlocked significant potential in automating domain-specific tasks. Despite their widespread adoption, fine-tuning LLMs for specialized applications remains an underexplored area. This study examines strategies for fine-tuning LLMs, emphasizing methodologies for adapting foundational models to specific domains such as landing page generation. Key considerations include dataset curation, preprocessing, model architecture selection, and efficient fine-tuning techniques like Low-Rank Adaptation (LoRA). The project focuses on designing an AI-powered web application that enables users to create customizable landing pages. By integrating pre-trained LLMs with modular design components, the system generates personalized content based on user prompts. It outlines the workflow for building this system, including the selection of layouts, themes, and components, along with AI-driven text generation for different sections of a landing page. The study also highlights the challenges encountered during fine-tuning, such as maintaining contextual relevance and optimizing computational efficiency. This work demonstrates the practical application of LLMs in web development, showcasing their potential to streamline content creation while meeting user-specific requirements. The findings contribute to advancing generative AI solutions and underscore the transformative role of LLM fine-tuning in modern web design.