A More Expensive Embedding Won't Save Your Traditional Chinese RAG: Three Layers of Failure and the Fix Order
Traditional Chinese RAG retrieval failures are a three-layer stack: embedding granularity defects (BGE/GTE from 0.1B to 7B all mis-rank on simple queries like 'fried chicken'), Simplified Chinese / English corpus dominance causing local vocabulary drift ('premium', 'exclusion clause' alignment is unreliable), and MTEB Chinese benchmarks being Simplified Chinese making model selection signals misleading. The fix is architectural: OpenCC normalization -> hybrid + jieba segmentation -> reranker -> local fine-tuning last -- and the prerequisite for all of it is building a Traditional Chinese eval set first.