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Endigest AI Core Summary
Lyft rebuilt its translation pipeline by integrating LLMs to reduce translation latency from days to minutes while maintaining linguist oversight.
•A dual-path architecture submits strings to both a Translation Management System (Smartling) and LLM workers simultaneously, enabling early release within 30 minutes for 95% of translations
•The Drafter uses a fast non-reasoning model to generate three distinct translation candidates per string, incorporating context metadata like UI placement and tone
•The Evaluator uses a reasoning-focused model to score each candidate on Accuracy, Fluency, Brand Alignment, and Technical Correctness, selecting the best or requesting revision
•Prompts are treated as version-controlled production code, and Pydantic schemas enforce structured output contracts between pipeline stages
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The pipeline was driven by real compliance requirements, including Quebec's Bill 96 mandating French-first UX and expansion into six European languages
This summary was automatically generated by AI based on the original article and may not be fully accurate.
Lyft
Google Cloud
Databricks
Stripe