This article describes the final “hygiene” step in an LLM-powered localization pipeline, focusing on programmatically cleaning and validating translated records. It covers removing unnecessary characters, normalizing Unicode, converting punctuation, detecting mismatches and unwanted Chinese characters, adjusting variable spacing, and ensuring all placeholders are present—ensuring translation quality before saving or patching to a TMS.

In the previous installments of this series, we established the foundation for an LLM-powered localization pipeline. In Part 2, we structured our approach further by transforming dictionary data, implementing chunking strategies, and creating translation guides to ensure consistency. We will dive into the critical elements of prompt engineering and validation – the components that determine how effectively our LLMs translate content and how we verify...

In the previous post, we explored how to use LLMs to generate translations in a localization pipeline. Which applied GPT-4o to genererate Full Dictionary Term Base (FDTB). We will continue to build on that foundation and introduce a more structured approach to localization.

Traditional Translation Management Systems (TMS) handle workflow logistics but still rely heavily on the translator’s cultural insight and deep knowledge of both source and target languages. In this blog post, we explore a modern architecture that integrates Large Language Models (LLMs), such as GPT-4o, with a traditional Translation Management System (TMS) to streamline and enhance the localization workflow. The workflow pipeline can ease this dependency...