Assessment of integrating LLM in website localisation service

This paper examines the integration of Large Language Models (LLMs) into website localisation services through a prompt-engineering-first approach. Traditional localisation methodologies - human translation, machine translation, and hybrid approaches - have established tradeoffs between quality, cost, and efficiency. The emergence of advanced LLMs offers a potential paradigm shift in addressing these challenges. The research presented explores how focusing on prompt engineering rather than post-translation editing can transform localisation workflows while maintaining high-quality outputs. Drawing on recent research in prompt engineering and machine translation efficacy, the paper establishes a theoretical framework for LLM implementation in localisation services. A practical case study involving the localisation of a cross-platform application demonstrates the implementation of this approach, including technical architecture, prompt design strategies, and testing methodologies. The findings indicate that LLM-powered localisation with well-engineered prompts can deliver comparable quality to specialised translation services while offering advantages in maintaining marketing tone, reducing implementation complexity, and supporting broader content creation needs across languages. The analysis extends beyond theoretical considerations to provide a decision framework for selecting appropriate localisation tools based on specific project requirements. The research concludes that for projects with marketing-focused content and moderate translation volume, the LLM approach with focused prompt engineering represents an optimal solution compared to traditional translation services and dedicated localisation platforms.