multilingual

Tokenization looks like plumbing until it explains your bill, your latency, and why the model fumbles numbers and rare languages. The signal is knowing how subword tokenizers work and the consequences that fall out of them.

Multilingual models are uneven across languages, and most candidates only name the obvious cause. The differentiator is the tokenizer: the same sentence can cost a non-English user several times the tokens. Here is the answer.

When a drug name or a Hindi word becomes nine subword tokens, you pay in cost, context, and accuracy. Adding tokens is easy and adding them well is not. Here is the tradeoff.

Every team ships English first, then finds that the retriever, the prompt, the latency budget and the cost model each degrade differently in the next five languages. The applied playbook looks nothing like the model-level answer, and the thing that sinks launches is the eval set nobody built.