Scaling Localization with AI at Lyft

This post details the iterative pipeline design for AI-powered localization, focusing on the 'Drafter' and 'Evaluator' components. It explains the rationale for using LLMs over traditional NMT, the benefits of a separate Drafter and Evaluator, and the structured handoffs between them. It also introduces the concept of treating prompts as version-controlled production code and provides sample prompts and outputs for the Drafter and Evaluator.